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Detecting and identifying manipulated portions within images poses a formidable challenge in research. Manipulated images, often created using image editing software such as Picasa or Photoshop, serve to obscure information and intentionally mislead viewers. Consequently, ensuring the authenticity of images becomes imperative prior to extracting meaningful data. One prevalent form of tampering is copy-move forgery, where objects are intentionally duplicated within an image using region of the same image. This study introduces a method for detecting copy-move forgery areas based on locating Scale-Invariant Feature Transform (SIFT) keypoints in images. The SIFT technique is employed for feature extraction, while feature descriptors are matched using brute force matching. Subsequently, a clustering algorithm is applied to group spatially proximate keypoints, enabling the detection of cloned regions. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is utilized to identify forged areas within the image. To mitigate erroneous forgery detection and reduce false positives, outlier detection techniques are employed. Experimental evaluations are conducted on the MICC-F220 and MICC-F600 datasets, with comparisons drawn against previously established methodologies. [ABSTRACT FROM AUTHOR]

The popularity of photography has grown as more individuals have access to camera equipment. Photos seem significant in our daily lives because they are rich with information. However, to learn more, images must often be enhanced. While many technologies are available to enhance the photographs, they are often used to produce phony shots, which leads to the spread of false information. The serious problem of image forgeries has to be addressed. To detect picture fraud, several well-established frameworks have been developed in the past. Recently, there has been an increase in the use of convolutional neural networks (CNNs), which has affected the localization of photo forgeries. One of the most difficult methods of picture falsification is image splicing, which involves cutting a section of one image into another. The picture forgery localization methods based on literature have a lot of drawbacks. As such, developing a technique for accurately and reliably identifying forgeries in photos that have been manipulated is crucial. We provide a potent deep learning-based approach that combines CNN with error level analysis (ELA) to detect picture fraud. To ascertain whether or not a picture has been manipulated, the ELA from an image is extracted and sent to CNN. Transfer learning with UNET (encoder-decoder model), and ELA is used to locate the tampered area. The recommended methods for predicting the changed location. The experiment results on the CASIA 2.0 database are quite encouraging, and these methodologies have been duly evaluated. The results of the recommended model outperform other well-known Transfer learning methodologies compared to the findings. The study suggests that the proposed method might get 0.917 and 0.935 accuracy and precision, respectively. Moreover, the graphical user interface—which enables us to determine the veracity of an image—is designed using these models. [ABSTRACT FROM AUTHOR]

• Comprehensive study of an alleged forgery of a tempera painting on tile with apparently Botticelli features. • Pigments identification – i.e., iron oxide pigments, cinnabar, carbon black, and calcite) - by combined FORS and Raman spectroscopy. • Pigments mapping by SCM algorithm application to the multispectral image cube. • Colorimetric analysis to quantitatively evaluate the cleaning method and provide support for an informed restoration process. In this work, a tempera painting of uncertain attribution from the Uffizi Galleries was studied. The painting, which portraits a man with apparently Botticellian features, was probably created by one of the most skilled forgers of the early 20th century, Umberto Giunti. He used a flat tile as a support, covered with preparatory layers emulating a wall painting. To shed light on the authorship of the painting, it was crucial to investigate its composing materials, as well as the execution technique and the state of conservation. To this end, non-invasive spectroscopic and imaging optical techniques were applied. Specifically, VIS-NIR multispectral reflectography was used to reveal the presence of possible preparatory drawings, pentimenti and overpaintings. Raman and Fibre Optics Reflectance Spectroscopies allowed identifying the pigments used by the artist. Finally, colour variations due to the cleaning procedure were documented in order to provide a support for an informed restoration process. [ABSTRACT FROM AUTHOR]

• Detection of cross-border series of fraudulent identity documents. • Realistic operating conditions for cross-border comparisons. • Concrete examples highlight the investigative and crime intelligence interest. The serial character of document fraud and its connection to organised crime groups who produce, sell and/or use fraudulent documents is a challenge for security and crime fighting. As a response, the added value of forensic intelligence is increasingly recognised. Using a forensic profiling method and a dedicated system deployed in Switzerland, document examiners can detect series (i.e., documents that share a common source) of fraudulent documents conveniently and efficiently. This detection can trigger or orientate investigations, supports crime intelligence efforts, and facilitates cross-jurisdictional cooperation. This study aims to assess the suitability of the forensic profiling system for international purpose and the efficiency of the method to detect cross-border series. The forensic profiling system has been deployed in France in the framework of a cross-border pilot project conducted by the School of Criminal Justice from the University of Lausanne and the French National Police (Division Nationale de Lutte contre la Fraude Documentaire et à l'Identité) over the period July 2019–May 2020. Data from the Swiss and French forensic profiling systems were compared to each other to detect cross-border series. The study sought to create operating conditions as close as possible to the real-life conditions of the profiling systems. The results are extremely positive both quantitatively and qualitatively. They demonstrate the benefit of setting up a systematic exchange of forensic data issued from profiling systems for fraudulent identity documents between France and Switzerland, let alone between any other countries. The results open up a very promising prospect for a sustained operational implementation by the police services of both countries and the extension of the exchanges internationally. [ABSTRACT FROM AUTHOR]

The conundrum of the ubiquitous deceptive reviews has overruled the online ontology with the obsession of obscure but obligatory posting of product reviews for the customers to believe, behold and beget the online product marketing. This mandates contemporary research in the direction to delve deeper on the application and analysis of deceiving online reviews with matured and advanced AI models functional on large scale datasets to effectively and efficiently demarcate between the genuine and the sham. The research counteracts the counterfeiting product reviews via the applications, assessment and analysis of the befitting AI models - Elastic-net Classifier model based on block coordinate descent with Wordcloud and its further performance enhancement through LightGBM Trees Classifier with Grid Search and Early Stopping support, with Log-Loss as performance metric for experimentation to gain insight into the intricacies of detection, diagnosis and diminution of fake product reviews. The paper also delineates discriminative and affirmative aspects of the dataset quality, statistics, stability and standards inherent and coherent to the creation of the dataset using Large Language Models (LLMs) intrinsic to the zeitgeist juncture of recent times promoting machines to produce large scale, cost effective bogus reviews in lieu of the Amazon Mechanical Turks. The results obtained with the Log-Loss holdout score of 0.1462 conforming the LightGBM classifier proves its performance better than the Elastic-Net classifier, conforming it as better than the ROC-AUC in terms of its proximity to the prediction probability for the matching actual/true value. [ABSTRACT FROM AUTHOR]

In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics. [ABSTRACT FROM AUTHOR]

This research paper presents an innovative approach to address the challenges associated with platitudinous paper certificates and electronic certificates. These challenges include issues such as preservation, management, verification inconvenience, poor reliability, and susceptibility to counterfeiting and tampering. The suggested approach involves an autonomous certificate system leveraging blockchain technology and smart contracts. Tailored for university students engaged in innovation and entrepreneurship competitions, this platform is intended to offer blockchain-based certification services. Within this system, various functions related to certificates, such as management, issuance, verification, and revocation, are executed through smart contracts. Structured data within these contracts stores information about signers, certificate templates, and certificate details. This structure simplifies the process of querying and validating certificates, all the while maintaining their security and authenticity. [ABSTRACT FROM AUTHOR]

Cross-Site Request Forgery (CSRF) is a prominent web exploit that continues to pose significant security risks, even on highly ranked websites. This research focuses on identifying the underlying vulnerability, understanding the techniques employed, and proposing effective preventive measures. The hybrid method which is systematic report review method and lab-based scenarios method is conducted to identify CSRF exploitation and prevention mechanisms. Using a scenario-based testing approach, this study investigated the cause and effect of CSRF attacks, uncovering numerous techniques for exploiting this vulnerability. Based on the report review, 35 CSRF vulnerability reports from the Hacker one Bug Bounty Platform are analyzed. The result shows there is a lack of awareness and implementation of primary defenses, resulting in a high violation of end-user data integrity. The result also extensively describes some of the profound causes and effects of CSRF attacks and uncovers a broad range of techniques for exploiting this vulnerability. Furthermore, this study provides actionable recommendations to enhance system security and raise awareness among developers and users. The findings from this research serve as a valuable resource for improving security practices and mitigating CSRF attacks. [ABSTRACT FROM AUTHOR]

Deepfake is a type of image and video face manipulation methods which could cause security and society threats. Although some related databases and detection models have been proposed for detecting face forgery media, achieving a generalizable detector for both known and unknown manipulations remains challenging. In this study, a novel deepfake detection model with high generalizability is proposed to tackle this issue. We employ supervised contrastive learning to enhance the generalizability to unknown manipulations and datasets. In addition, we design a cross-modality data augmentation method by combining SRM and RGB features to extract detection clues comprehensively. Furthermore, we propose a multi-scale feature enhancement module to enhance textural and semantic information. Extensive experiments have demonstrated that our method improves model generalization in both intra- and cross- dataset scenarios. [ABSTRACT FROM AUTHOR]

As an important and challenging problem, Face Forgery Detection has gained considerable attention. Usually, it suffers from the diversity of forgery patterns in forgery images, which requires a detection model to have capability of capturing various patterns in the challenging scenarios. To address this problem, we present a divide-and-aggregate learning framework to build multi-expert models and integrate them into a unified model. Firstly, the built multi-expert models are pre-trained to capture and preserve the specific forgery pattern produced by each manipulation method separately. Secondly, to transfer diverse knowledge of experts, we propose an integrating approach based on knowledge distillation. However, the difference of manipulation-aware knowledge among these experts concerns the way of distillation when the knowledge is combined in the only student model. Thus, to determine the importance of each expert, we propose a sample-aware Adaptive Learning from Experts strategy (ALFE) to assign adaptive expert distillation weights for each fake sample based on the predictions of each expert. Experiments show that our method achieves SOTA performances on ACC/AUC in the benchmark of FaceForensics++, demonstrating the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The widespread dissemination of facial forgery technology has brought many ethical issues and aroused widespread concern in society. Most research today treats deepfake detection as a fine grained classification task, which however makes it difficult to enable the feature extractor to express the features related to the real and fake attributes. This paper proposes a depth map guided triplet network, which mainly consists of a depth prediction network and a triplet feature extraction network. The depth map predicted by the depth prediction network can effectively reflect the differences between real and fake faces in discontinuity, inconsistent illumination, and blurring, thus in favor of deepfake detection. Regardless of the facial appearance changes induced by deepfake, we argue that real and fake faces should correspond to their respective latent feature spaces. Particularly, the pair of real faces (original–target) remain close in the latent feature space, while the two pairs of real–fake faces (original–fake, target–fake) instead keep faraway. Following this paradigm, we suggest a triplet loss supervision network to extract the sufficiently discriminative deep features, which minimizes the distance of the original–target pair and maximize the distance of the original–fake (also target–fake) pair. The extensive results on public FaceForensics++ and Celeb-DF datasets validate the superiority of our method over competitors. [ABSTRACT FROM AUTHOR]

Image forensic analysis becomes a major role in the field of digital image security due to tampering and forgery. The image forgery violates the authenticity and ownership of digital images. Copy-move forgery considers a significant kind of image forensic analysis algorithm. In this kind, the forger copies a part of an original image and then pastes it into the selected position from the same image. The purpose of forgery is to hide or highlight a specific region of the original image. To detect copy-move forgery, there are two traditional techniques: block-based and keypoint-based. The main drawback of the keypoint-based technique is the insufficient features for the small and flat regions, which causes undetected forgery. In contrast, the block-based technique has intensive processing. Therefore, this paper proposes a robust scheme that overcomes the drawbacks of the above techniques and maintains their advantages. This scheme adopts three connected stages, the first detects the initial duplicated regions using the SURF-HOG detector and descriptor. Subsequently, the second stage localizes the primary matched regions by SLIC segmentation and then selects the suspicious neighbor regions to be combined with primary regions to obtain the active regions. In the third stage, the block-based technique adopts overlapping Zernike moments to extract sufficient key points from the produced active regions. In the final stage, the duplicated regions are classified into authentic or forged regions. The proposed scheme provides not only forgery detection but also localization and recognition for the duplicated regions. The experimental results show that the proposed scheme is fast and has high accuracy for forgery detection and localization, at least 93.75, and 7.25 in terms of True Positive and False Positive Rates. Moreover, the scheme has high robustness under various conditions and attacks such as geometric transformation attacks and compound photometric attacks. The proposed scheme can be used in sensitive applications such as cybercrime detection and adopted as evidence in the courts. [ABSTRACT FROM AUTHOR]

• A deep learning-based method is presented for detecting copy-move image forgery. • The generative adversarial networks are used for image quality enhancement. • A tailored dual-branch CNN architecture is used for forgery detection. • The proposed method is able to detect several forged areas, simultaneously. • The proposed method is able to determine the source and target of the forgery. • The proposed method is robust against various pre-processing/post-processing attacks. One of the prevalent methods of image forgery is copy-move, where one or more regions of an image are duplicated and moved elsewhere in the image. It is usually difficult to detect this type of forgery due to the similarity of the copied and forged areas. Also, forgers perform pre-processing and/or post-processing operations on the manipulated regions to make it even more difficult to detect. In this study, an image quality-independent method based on deep learning approach, termed QDL-CMFD, is presented for detecting this type of forgery. QDL-CMFD utilizes generative adversarial networks for image quality enhancement, and convolutional neural networks (CNN) for forgery detection. A tailored dual-branch CNN architecture is introduced consisting of two subnetworks, namely a manipulation detection subnetwork and a similarity detection subnetwork. Accordingly, unlike most existing methods, QDL-CMFD is able to simultaneous detection of several forged areas, as well as determining the source and target of the forgery. Also, QDL-CMFD is robust against various pre-processing/post-processing attacks. It shows excellent performance for detecting low-quality forged images and small areas. Experiments conducted on the CASIA and CoMoFoD benchmark datasets confirm that QDL-CMFD performs significantly better than the competitors. All the implementation source codes of QDL-CMFD are available at https://github.com/MehradAria/QDL-CMFD. [ABSTRACT FROM AUTHOR]

Counterfeit and falsified medicines have become a threat to public health around the world. The objective of this review study is to analyze all the relevant studies on preventing or reducing falsified and counterfeit medicines through digital intervention following a Systematic Literature Review (SLR) approach. A total of 51 articles were reviewed from an initial set of 1253 articles following an inclusion-exclusion criterion. As an outcome, this review study found that falsified and counterfeit medicines have become a crucial issue for research and investigation over time. Various advanced technologies (like Blockchain, IoT, RFID, image processing, pattern recognition, etc.) are being used to fight against this issue efficiently. The review also reveals future research opportunities to facilitate the existing initiatives for preventing medicine counterfeit that includes: exploring the implications of emerging technologies; discovering the contaminated point over the medicine supply chain; investigating the less emphasized concern of counterfeit and falsified medicines; exploring all possible use-cases or features of any digital solution to reduce falsified and counterfeit medicines; and the development of counterfeit/ falsified incidents reporting system. Thus, the implication of this study is to discover the research gaps and provide future research directions focusing on the prevention of usage of falsified and counterfeit medicines through the effective use of Information and Communication Technology (ICT). [ABSTRACT FROM AUTHOR]

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of publicly available benchmark datasets for malicious manipulation detection. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena. [ABSTRACT FROM AUTHOR]

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains. [ABSTRACT FROM AUTHOR]

Current image tampering detection methods rely on various forgery footprints, such as JPEG artifacts and edge inconsistencies, and use algorithms related to image segmentation. However, these methods have several issues, including over-fitting, focusing on only a few specific forgery footprints, and emphasizing semantically relevant information while ignoring tampering traces. This paper proposes a model for image manipulation detection and localization based on multi-scale contrast learning(MSCL-Net). The model utilizes the differences in feature distributions between tampered and untampered regions to extract a comprehensive tamper trace. It uses a dual-stream structured encoder that incorporates both RGB raw images and SRM noise features. A Feature Cross-Fusion Module (FCFM) is proposed to fuse features for improving feature representation of tampered information. The decoding process involves the use of an Adaptive Self-Attention Module (ASAM) to filter and aggregate relevant context from coarse feature maps. Additionally, a Supervised Contrastive Learning Module (SCLM) is used to expand the difference between tampered and untampered areas. The loss function for multi-loss fusion comprises classification loss, segmentation loss, and multi-scale supervised contrastive loss. This improves the network's understanding of global differences, reduces false positives, weakens semantic information, and enhances the model's ability to locate tampered regions of varying sizes. Extensive experiments are conducted across multiple datasets, demonstrating that our model is robust against attacks and resilient to false-positive predictions at both the image-level and pixel-level. Furthermore, its overall performance exceeds that of state-of-the-art alternatives in reliably detecting and localizing tampered images. • A feature cross fusion module(FCFM) is used to fuse dual-stream features(RGB and RSM). During the decoding process, the Adaptive Self-Attention Module (ASAM) is utilized to filter and aggregate the relevant context from coarse feature maps. • Supervised Contrast learning is employed to weaken semantic-related information and highlight the dissimilarity in feature distribution between untampered and tampered regions. • The proposed method use three loss: classification loss emphasizes sensitivity and specificity, avoids false detection of untampered images; segmentation loss assists the model to accurately localize the tampered region; contrastive loss enhances the model's capacity of localizing tampered region with different sizes. • Extensive experiments conducted across multiple datasets demonstrate that our model exhibits robustness against attacks and resilience to false-positive predictions at both the image-level and pixel-level. [ABSTRACT FROM AUTHOR]

With a simple forgery technique but a realistic result, video copy-move forgery has currently become one of the most popular tampering manners. In the last couple of years, various new techniques deriving from machine intelligence and pattern recognition have been widely proposed for image forensics. However, it still faces a very challenging task in the field of video copy-move forgery for four reasons: i) Low F 1 score and high false-alarm ; ii) Lack of a synthesis processing framework; iii) Weak detection robustness and accuracy; iv) Low efficiency. A novel Hierarchical Coarse-to-Fine framework for effective video copy-move forgery detection is proposed to overcome these challenges: i) In the coarse forgery frame-pair matching, the coarse copy-move frame-pairs matching algorithm with the newly proposed two-pass filters can locate real forgery frame-pairs (FFP) and also reduce false-alarm. ii) Through further analysis of the actual FFP, the detection of intra-frame and inter-frame copy-move forgeries can be accurately and simultaneously determined. iii) In the fine keypoint-pairs matching, our newly designed two-hierarchical keypoint-pair filtering can accurately localize the forgery region at pixel level under various adverse conditions. iv) The novel Hierarchical Coarse-to-Fine framework (together with the newly designed algorithms above) considers only the real FFP and true keypoint-pairs for computation, resulting in higher efficiency and accuracy. Finally, Delaunay Triangulation-based region filling is employed to indicate the forgery regions. Compared to the latest methods, our algorithm has been tested extensively and found to be the best at detecting forgeries, with a top score of F 1 =0.77 and no false-alarms , even under different types of attacks, as validated by the well-known GRIP dataset. [ABSTRACT FROM AUTHOR]

In this paper, we propose an uncertainty-aware hierarchical labeling method for face forgery detection, which aims to simultaneously explore the traces of forgery contents from a hierarchical level. Unlike existing face forgery detection methods that usually focus on local regions or entire images, our proposed approach takes advantage of hierarchical labeling as auxiliary supervised signals to capture hybrid information from pixel-level, patch-level and image-level. Moreover, we utilize Transformer architecture to extract the patch-level information to build a bridge between the pixel-level and the image-level information. However, the face forgery ground truth always carries data uncertainty due to the existence of the blending step and the compression process during face image manipulation. Thus we introduce an uncertainty learning method to formulate and leverage the data uncertainty. Extensive experimental results on five public datasets demonstrate that our approach not only achieves very competitive performance but also improves the generalization ability. • We are the first to introduce uncertainty learning for face forgery detection. • We propose the hierarchical labeling as auxiliary supervised signals to exploit the multi-level information to maximize the generalization of our framework. • Experimental results on several datasets show that our proposed approach achieves very competitive performance and strong generalization ability. [ABSTRACT FROM AUTHOR]

Ongoing breakthroughs in the development of functional printing materials are leading to rapid and widespread industrialization of three-dimensional (3D) printing, accompanied by increasingly urgent requirements for methods to prevent problems such as tampering, counterfeiting and destruction of 3D printed products. Anti-counterfeiting of 2D printed products has a long history, but its methods are not suitable for application to 3D printed products, as the latter products have embedding that is substantially different to that of the former products. This review article analyses anti-counterfeiting techniques for 2D printed products, proposes two embedding strategies for 3D printing based on material-responsive properties and 3D digital information, and summarizes the progress and performance of the corresponding anti-counterfeiting methods. It is shown that among the embedded anti-counterfeiting methods that exploit the responsive properties of materials, methods based on optical properties, spectral properties and deformation properties of 3D printing materials are the focus of research on embedding anti-counterfeiting materials into 3D printed objects. In addition, it is demonstrated that state-of-the-art embedding-based anti-counterfeiting methods use 3D digital information interactions and depend on 3D digital watermarks, 3D identification codes and radio-frequency tagging. Finally, a detailed discussion is provided on the generation, integration, extension, detection and prediction of embedded security features that can be printed synchronously with a functional structure. This offers a unique perspective on standardization of embedding-based anti-counterfeiting methods used in 3D printing. • Propose two embedding strategies for 3D printing based on material-responsive properties and 3D digital information. • Summarize the progress and performance of anti-counterfeiting methods for 3D printing. • State embedded security features printed synchronously with a functional structure. • Offer a perspective on standardization of embedded anti-counterfeiting methods for 3D printing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel methodology for unalterable labelling via subcutaneous coding based on embedding WC particles in a Ti base material. • Analysis of W content and dispersion on the melted Ti matrix, resulting X-ray readability and increased local density. • Validation by a demonstrator where Ti6Al4V-coating and subsequent grinding show no negative impact on code readability. Part identification has become a priority in manufacturing, especially when it comes to the additive manufacturing (AM) industry, where the counterfeit printing of almost any digitalized component is possible. As a means of avoiding forgery, the development of non-detectable labels, or digital passports is necessary. In the present work, a novel methodology for printing subcutaneous coding on aeronautical parts is proposed thanks to the multimaterial capabilities of the laser based directed energy deposition (DED-LB). The coding is based on embedding a dot pattern of a high-density alloy on a small area of a lower density component, which once covered and finished generates a pattern only visible by X-ray imagining. The viability of the proposed methodology is proven by embedding WC particles on a Ti6Al4V substrate. Firstly, the most relevant process parameters are optimized to ensure a sharp and a readable code, and their geometry is analysed by means of industrial Computed Tomography (CT). Also, the metallurgical quality and chemical composition of the generated dots is evaluated by Scanning Electron Microscope (SEM). Finally, a demonstrator part is fabricated with a hidden code. The code readability and the mechanical properties are tested to ensure the feasibility of the developed methodology. [ABSTRACT FROM AUTHOR]

• An electrochemical sensor for Xanax is presented to tackle counterfeit pills. • The active ingredient, alprazolam, is identified via electrochemical fingerprinting and data analysis. • Adulterants and illicit drugs are integrated in the automated electrochemical strategy. • The counterfeit testing is based on real on-site scenarios, i.e. affordable and user-friendly. • The protocol can be expanded to other counterfeit medicines. The illicit drug market has been constantly evolving in the last decades, with a significant rise in counterfeit medicines posing serious public health risks. Benzodiazepines (BZDs) such as alprazolam (generally sold under the brand name Xanax), have particularly become the target of counterfeiting efforts due to their addictive nature and upsurge of unregulated designer BZDs. These counterfeit versions frequently resemble legitimate products but contain harmful adulterants or other potent illicit substances. Few methods have been developed to tackle counterfeit pills, usually limited to accurate and sophisticated laboratory equipment. This study explores the feasibility of combining electrochemical fingerprinting with data analysis to overcome the limitations of traditional methods. First, the electrochemical behavior of selected BZDs is studied, and analytical parameters such as pH are optimized. Then, the electroanalysis of common adulterants and illicit drugs is addressed and integrated into a user-friendly app, including a flowchart system. The proposed electrochemical strategy enables the detection of counterfeit Xanax by identifying the presence or absence of alprazolam. It also allows determination of the alprazolam content within a pill while meeting the fundamental requirements of the end users. This study represents an on-site methodology to address the growing challenges posed by BZDs, easily transferable to counterfeit medicines from other drug groups. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, the enhanced abilities of diffusion models have led to more realistic inpainting results, which raises the potential for criminal activity through image forgery. In this study, we explore the detection of inpainted images generated by a diffusion model. We propose a method for inpainting localization using an inter-intra similarity (IIS) module based on image frequency features. The proposed IIS module learns the inter-patch relationship through the learnable frequency filter and subsequently covers the intra-patch relationship through the self-similarity operation. We provide the Diffusion Model Inpainting Dataset (DMID), a benchmark dataset comprising inpainted images using four different diffusion models and three types of masks. Additionally, a test dataset that includes three sampling steps is provided. We validated the effectiveness of our proposed approach by conducting comparative tests with existing forgery detectors using our dataset and testing the robustness of JPEG compression. Additionally, we tested our proposed method on datasets with different sampling step sizes. Our work provides a starting point for research on the detection of inpainting-based forgery using diffusion models. Additionally, by openly releasing the dataset, we offer an opportunity to advance future in-depth research related to forensics. [Display omitted] • We demonstrate the localization of inpainting images generated by diffusion models. • We introduce a new image forensics dataset based on diffusion modelbased inpainting. • We propose an inter-patch relationship through the learnable frequency filter layer. • Inpainting localization achieved using intra-patch relationships via selfsimilarity. • We verify the effects using comparative experiments with external datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • Non-target HS-SPME-GC–MS fingerprinting distinguishes whiskey authenticity. • Chemometrics tentatively identify VOCs markers of counterfeiting in whiskeys. • Potential consumers often miss aroma and color cues in counterfeit whiskeys. This study presents a method employing gas chromatography coupled with mass spectrometry and headspace solid-phase microextraction (HS-SPME-GC–MS), supplemented with chemometrics (Soft independent modelling of class analogies - SIMCA), to analyze volatile organic compound (VOCs) profiles in suspect whiskey samples. Furthermore, a sensory analysis of aroma and color was conducted with a panel of 52 non-trained volunteers to evaluate their ability to discriminate and preference for counterfeit whiskeys. The HS-SPME-GC–MS method successfully distinguished 41 seized samples from authentic beverages. Interestingly, sensory analysis revealed that panelists could differentiate between counterfeit and authentic samples with a reference standard but did not consistently show a preference for aroma. In some cases, there was even a preference for the color of counterfeit whiskeys. The findings suggest that sensorial tests alone may not effectively distinguish counterfeit from authentic whiskeys, especially for non-expert consumers, highlighting the need for analytical instrumentation methods in fraud detection. [ABSTRACT FROM AUTHOR]

While the misuse of Deepfake technology is drawing growing concern in the literature of information security, related forgery detection has become a significant challenge in practical applications. Most state-of-the-art detection methods achieve satisfactory results on raw images, but their performance drops significantly on processed images (e.g. compression). In this work, we propose a novel Deepfake detection method that integrates spatial and frequency domain information within a knowledge distillation framework for efficient forgery detection. Our method consists of two steps: (1) spatial-frequency fusion, and (2) multi-knowledge distillation. We first extract frequency-domain and spatial-domain features, then fuse them and utilize them in attention-based guidance to improve the classification results. Note that the spatial-frequency fusion serves as the basis for both the teacher and student models with spatial-frequency features and logits transferred as knowledge. We conducted comprehensive experiments on several benchmark datasets which successfully demonstrate the excellent generalization performance of our method on compressed images while outperforming state-of-the-art techniques. • Utilize two-stream fusion for comprehensive spatial-frequency domain exploitation. • Attention-guided fusion improves feature discriminability for robust classification. • Teacher model features and logits guide student model for key forgery detection cues. • Experiments show strong generalization of our method on compressed images. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electrospun nanocrystalline cellulose/polysulfone nanofibers were prepared. • Nanoparticles (5–14 nm) of strontium aluminate were encapsulated in nanofibers. • Nanofibrous membranes showed mechanical reliability and photostability. • Different phosphor contents provided nanofibers with distinctive optical activity. • Colorless nanofibrous membranes showed photochromism to green (519 nm) under UV. Photochromic inks have been used as an encryption technology to improve the authentication of commercial products. However, they have recently shown poor durability and photostability. In this context, an anti-counterfeiting photochromic nanofibrous membrane was developed for data encoding. Rare-earth doped aluminate nanoparticles (RAN) were embedded in a nanofibrous labeling system composed of a cellulose nanocrystal-reinforced polysulfone nanocomposite (PSU). Cellulose nanocrystals were prepared from microcrystalline cellulose, displaying diameters of 10–20 nm. RAN exhibited diameters of 5–14 nm, and PSU fibers displayed diameters of 75–150 nm. Impressive photostability, durability, and reversibility were achieved by the RAN-immobilized nanofibrous label. The emission properties of the photoresponsive nanofibrous membrane were tailored by manipulating the amount of RAN. The nanofibrous membrane was colorless under visible lighting conditions, whereas a green emission was detected when exposed to ultraviolet radiation. An emission peak of 519 nm indicated a green color, whereas an excitation peak of 365 nm suggested transparency. In order to identify the chemical composition and structural properties of the produced nanofibrous membranes, different microscopic and spectroscopic techniques were used. The mechanical performance of the paper sheets coated with RAN@PSU nanofibers was investigated. The current strategy provides a simple procedure for the development of nanofibrous membranes, which can assist to combat forging of commercial products. [ABSTRACT FROM AUTHOR]

Recently, Gong et al. proposed a Certificateless Aggregate Signature (CLAS) scheme (shorted by PCAS) with conditional privacy-preserving without bilinear pairing for Vehicular Ad-hoc Networks (VANETs). Unfortunately, after analyzing security, the PCAS scheme fails to satisfy the necessary unlinkability in VANETs and is vulnerable to the public key replacement forgery attack of the Type-I adversary. Subsequently, this article analyzes the causes of these two security issues in detail and proposes an improved CLAS scheme for VANETs (called IPCAS). Through security analysis, this article proves that the IPCAS is existentially unforgeable under the adaptive chosen message attacks against Type-I and Type-II adversaries in the random oracle model, and satisfies the necessary security and privacy requirements in VANETs, including unlinkability. Finally, the performance analysis results show that compared to PCAS for single message and n aggregate messages (n = 1000) , the computational overhead of IPCAS is reduced by 20.01% and 49.08% respectively, and the communication overhead is reduced by 18.75% and 28.14% respectively. Therefore, IPCAS not only makes up for the security vulnerabilities of PCAS but also has better performance. [ABSTRACT FROM AUTHOR]

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image's Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only. • Image splicing detection using Texture and Haralick features from GLCM. • Achieved accuracy of 95% and AUC score of 0.99. • Feature set is optimized with dimension 15 only. • Non-overlapping block segmentation for splicing localization. • Low computational overhead as it is feature-based with low-dimension. [ABSTRACT FROM AUTHOR]

• A novel method for fast and accurate classification of counterfeit banknotes. • SD-OCT was employed for sub-surface imaging and quantitative assessment. • 290 samples were used and 16 quantitative OCT features were extracted. • The SVM classifier has achieved the highest classification performance. • A potentially useful tool for identifying the sources of counterfeit banknotes. Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes. [ABSTRACT FROM AUTHOR]

• Systematic method for the profiling of fraudulent identity documents. • Visual characteristics of digitised images. • Method evaluated in real work conditions. Document fraud is a transnational form of crime, and its serial character has already been highlighted. To combat this phenomenon, the Interstate Database of Fraudulent Identity Documents (BIDIF) has been created and implemented in Switzerland. It supports the comparison of documents and the detection of series, i.e., documents that share a common source. To efficiently use such a system, forensic document examiners would benefit from a harmonised and proven profiling method. Thus, the aim of this study is to develop a method for comparing documents and establishing series. The method is meant to improve the detection capabilities of forensic document examiners operating BIDIF or engaged in the profiling of fraudulent documents. First, a method based on the visual characteristics of digitised images of fraudulent identity documents has been developed. Subsequently, the method was qualitatively and quantitatively evaluated using four tests. The first test verified the ability of the method to detect pre-existing series. The second test checked the capability of the method to detect links amongst isolated documents. Finally, two further tests were carried out to compare the method impact on the successful detection of series. These tests were carried out by professional forensic document examiners and Master students in forensic science, respectively. This allowed a comparison of the method influence on series detection. The method allowed a significant increase in the number of series and links detected, while also decreasing the occurrence of false negatives and false positives. Furthermore, links were more rapidly detected. [ABSTRACT FROM AUTHOR]

• DeepFake detection is usually regarded as a binary classification task. • It is important to pay attention to the local discriminative features of the tampered images. • For patch-based solutions, resizing each patch before feeding it into the CNN may result in the loss and change of its original details. • Facial Patch Mapping strategy maintains the spoof patterns of each patch to the greatest extent, while also improving the training and inference speed. • Local voting scheme improves the detection accuracy. Facial forgery by DeepFake has recently attracted more public attention. Face image contains sensitive personal information, abuse of such technology will grow into a menace. Since the difference between real and fake faces is usually subtle and local, the general detection framework of applying the backbone network to capture the global features of the entire face and then feeding it into the binary classifier is not optimal. In addition, patch-based schemes are widely used in various computer vision tasks, including image classification. However, how to extract features for location-specific and arbitrary-shaped patches while preserving their original information and spoof patterns as much as possible requires further exploration. In this paper, a novel deep forgery detector called Patch-DFD is proposed, which applies a patch-based solution of Facial Patch Mapping (FPM) to obtain several part-based feature maps, preserving original details of each facial patch to the greatest extent. Besides, the BM-pooling module aims to fix the size of the feature maps while reducing quantization errors. The local voting strategy is finally used to fuse the results of parts detectors, so as to more accurately identify the fake faces generated by deep generative models. Compared to typical patch-wise framework that takes patch inputs, our scheme is more efficient due to the absence of repeated convolution operations. Moreover, extensive experiments conducted on publicly available face forensics datasets have proved that the effectiveness of our framework. [ABSTRACT FROM AUTHOR]

• We present a systematical study on forgery detection about unknown types of forgery technologies. • We propose a novel method named MAFD, which can effectively learn transferable knowledge across fake domains. • MAFD can generalize significantly better to unknown forgery types, and is more robust to image distortions. Face forgery detection is an important yet challenging task that aims to distinguish whether a face video has been modified. As various types of face forgery are constantly produced and made available, existing methods usually overfit to the manipulation methods they are trained for, and cannot generalize well to detect the unseen or unknown forgery types. To address this issue, we present a systematic study on a more generalizable solution of face forgery detection, which endows the model an ability to recognize fake videos with unpredictable forgery types. Specifically, we develop a model-agnostic learning approach with a gradient-based meta-train and meta-test procedure to simulate the domain shift from known to unknown forgery types. To further emphasize the relative importance of different available forgery types during training, we propose a self-guided importance sampling strategy, which is integrated with a general video-level classification network. We also build a dataset with a wide range of 10 different forgery types to benchmark the generalization ability of face forgery detection. Extensive experiments on multiple testing protocols of evaluating generalization ability show that our method generalizes significantly better on unknown forgery manipulations. [ABSTRACT FROM AUTHOR]

• 17 Laboratories from 16 countries conducted a forensic round robin study. • Six printer, paper and ink tasks had to be covered on three document pages. • All tasks except the age of the signatures could be solved. • Realistic scenarios are necessary for a realistic assessment of method performance. • In multimodal studies, the same samples must be examined from all participants. The forensic scenario, on which the round robin study was based, simulated a suspected intentional manipulation of a real estate rental agreement consisting of a total of three pages. The aims of this study were to (i) establish the amount and reliability of information extractable from a single type of evidence and to (ii) provide suggestions on the most suitable combination of compatible techniques for a multi-modal imaging approach to forgery detection. To address these aims, seventeen laboratories from sixteen countries were invited to answer the following tasks questions: (i) which printing technique was used? (ii) were the three pages printed with the same printer? (iii) were the three pages made from the same paper? (iv) were the three pages originally stapled? (v) were the headings and signatures written with the same ink? and (vi) were headings and signatures of the same age on all pages? The methods used were classified into the following categories: Optical spectroscopy, including multispectral imaging, smartphone mapping, UV-luminescence and LIBS; Infrared spectroscopy, including Raman and FTIR (micro-)spectroscopy; X-ray spectroscopy, including SEM-EDX, PIXE and XPS; Mass spectrometry, including ICPMS, SIMS, MALDI and LDIMS; Electrostatic imaging, as well as non-imaging methods, such as non-multimodal visual inspection, (micro-)spectroscopy, physical testing and thin layer chromatography. The performance of the techniques was evaluated as the proportion of discriminated sample pairs to all possible sample pairs. For the undiscriminated sample pairs, a distinction was made between undecidability and false positive claims. It was found that none of the methods used were able to solve all tasks completely and/or correctly and that certain methods were a priori judged unsuitable by the laboratories for some tasks. Correct results were generally achieved for the discrimination of printer toners, whereas incorrect results in the discrimination of inks. For the discrimination of paper, solid state analytical methods proved to be superior to mass spectrometric methods. None of the participating laboratories deemed addressing ink age feasible. It was concluded that correct forensic statements can only be achieved by the complementary application of different methods and that the classical approach of round robin studies to send standardised subsamples to the participants is not feasible for a true multimodal approach if the techniques are not available at one location. [ABSTRACT FROM AUTHOR]

• We study different anti-counterfeit strategies with deceptive counterfeits. • We endogenize anti-counterfeit and counterfeit hiding efforts under these strategies. • Imposing heavier penalty on the counterfeit may hurt the authentic firm. • Deeper counterfeit penetration or closer imitation may benefit the authentic firm. • The authentic firm prefers to adopt a reactive anti-counterfeiting strategy in equilibrium. This papers studies anti-counterfeiting strategies with deceptive counterfeits. We develop a model to study the interactive anti-counterfeiting and counterfeit hiding decisions between the genuine brand company and a deceptive counterfeiter. Facing deceptive counterfeits, the genuine brand company can choose to adopt a proactive, instantaneous, or reactive anti-counterfeit strategy. We first examine these strategies and then characterize the equilibrium outcomes. Surprisingly, we reveal that the genuine brand company does not necessarily benefit from heavier penalty and the counterfeiter is not necessarily worse off with more costly counterfeit hiding efforts. Interestingly, the counterfeiter's hiding effort decreases with heavier penalty when the genuine brand company's anti-counterfeit and the counterfeiter's hiding decisions are sufficiently efficient. Besides, higher levels of counterfeit imitation or penetration can either hurt or benefit the genuine brand company. Whether the counterfeiter is better off with deeper counterfeit penetration depends on the status quo. Furthermore, the counterfeiter exerts fewer counterfeit hiding efforts when the genuine brand company exerts more anti-counterfeit efforts, while the anti-counterfeit effort increases with the counterfeit hiding effort, regardless of the anti-counterfeiting strategies. Finally, for decision makers and policy makers, we discuss the implications for anti-counterfeiting strategies in practice. [ABSTRACT FROM AUTHOR]

Many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these methods focus on validating local patches or local objects. We regard image splicing forgery detection as a binary classification task that distinguishes tampered and non-tampered regions by forensic fingerprints rather than semantic features. As the network goes deep, its representation ability becomes strong. However, the non-semantic forensic fingerprints can hardly be retained by normal CNNs in deep layers. We proposed a novel dual-encoder network (D-Net) for image splicing forgery detection to resolve these issues, employing an unfixed and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides structural information that assists the learning and detection of the forgeries. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Net for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Net and state-of-the-art methods, D-Net, without pre-training or training on a large number of forgery images, outperformed the other methods in pixel-level forgery detection. Moreover, it is stably robust to different anti-forensic attacks. • The spatial pyramid global-feature extraction module expands the global in-sight. • The unfixed encoder autonomously learns the image fingerprints. • The fixed encoder intentionally provides the structural information of the pixels. • Not require a large number of training samples and a pre-training step. [ABSTRACT FROM AUTHOR]

Image manipulation localization is a binary segmentation task that sensitive to the tampered artifacts other than awareness of the object. Thus, both traditional and learning-based methods highly rely on hand-crafted features. However, these specifically-defined features limit the ability of the network for general scenes. To tackle this problem, we propose a dual homology-aware generative adversarial network (DH-GAN), a novel GAN-based framework to localize the manipulated region. Firstly, we localize the forgery region via re-calibrating the multi-scale encoded features with a selective pyramid generator. Then, we perform the homology identification in the discriminator. The proposed homology-aware discriminators contain a stack of masked convolution (MConv) layers and learn to identify the real/fake of the segmented pixels on the predicted/target masked image in a hard-gating manner. Overall, the networks are optimized under a standard GAN. Experiments show that the proposed method outperforms other state-of-the-art algorithms on four popular image manipulation datasets. • Avoiding the use of hand-crafted features for image manipulation localization. • Learning generalized features by identifying the homology of the pixels. • Re-calibrating multi-scale features with cross-scale information interaction. [ABSTRACT FROM AUTHOR]

Recently, the applications and artificial intelligences used for image manipulation have become quite successful. In this case, the manipulation of personal data can lead to problems of insurmountable magnitude. Such problems not only put personal data at risk, but also lead us to unethical practices, with potentially irreversible negative consequences. For this reason, the reliability of image or video data is highly questionable. To solve this challenging problem, we introduce a Visual Transformer based Visual Transformer with Variational Autoencoder Network (ViT-VAE Net) model. The model includes Visual Transformer, one of the state-of-the-art architectures. In addition to this architecture, a Variational Auto Encoder structure is also included. is much more effective than models developed with the classical Convolutional Neural Network (CNN). Unlike models developed with CNN, it can perform operations on images of any size without being bound by a standard image resolution. In addition, thanks to the self-attention mechanism in the Visual Transformer architecture, manipulations on the image are caught more easily than CNN. The ViT-VAE Net model was trained with a large dataset and tested with 4 different datasets. With a success rate of 67 % on the training dataset, the model provided promising results. Very high rates were also obtained with the test datasets. • IFDwT is a model developed to detect and localize manipulations made on images. • The model is based on the state-of-the-art Visual Transformer architecture. • The model can make predictions at all image sizes regardless of image size. • The model does not need many computational units. [ABSTRACT FROM AUTHOR]

The mechanochromic luminescent material with high quantum yield is constructed by a twisting D-A-D conjugated molecule, which possesses a special hybridized local and charge transfer excited state. Based on the multi-mode output signals during the grinding process, the written tags using this material exhibit high stability in various harsh environments and demonstrate superior anti-counterfeiting performance in practical applications. [Display omitted] • A mechanochromic luminescent material with the highest quantum yield in the reported MCL materials is developed. • A D-A conjugated molecule with the HLCT excited state features is utilized to design the mechanochromic luminescent material. • The mechanochromic mechanism of force-induced multilevel charge transfer is revealed by the cooperation of experimental characterization and DFT calculations. • The transformation of molecular conformation during stretching is monitored. • The tags written by this material show high stability and multi-mode output signals in practical applications. The multi-mode anti-counterfeiting materials, constructed by mechanochromic luminescent materials with high quantum yield, can effectively prevent illegal counterfeiting, but their design and construction still present significant challenges. Herein, a twisting D-A-D conjugated molecule, FBtF, is obtained with a special hybridized local and charge transfer excited state, and shows approximately 100 % photoluminescence quantum yield in the coating film. The self-assembled FBtF microplates exhibit bathochromic-shifted emission from green to orange, accompanied by the photoluminescence quantum yield increase from 47.8 % to 89.0 % upon grinding, which is the highest in reported MCL materials, to the best of our knowledge. In contrast, a D-π-D type molecule (FBenF) and a D-D'-D molecule (FAnF) maintain the blue-emission after grinding. Through mechanistic investigation, the force-induced molecular conformational planarization and the breakage of the intermolecular charge transfer collaboratively lead to the emission-color change and photoluminescence quantum yield increase. The FBtF microplates are designed as ink for anti-counterfeiting applications based on multi-mode output signals, including the UV light-excited image and the grinding-induced emission color change together with the photoluminescence quantum yield increase. The written tags exhibit high stability in various harsh environments, and demonstrate superior anti-counterfeiting performance in practical applications, such as medicine anti-counterfeiting. [ABSTRACT FROM AUTHOR]

[Display omitted] • First use of ToF-SIMS with PCA and MCR for forensic ink analysis. • Clear differentiation of six blue pen inks via advanced statistical methods. • Non-destructive identification of pen strokes in forgery cases. • MCR provides exact pen stroke ID based on unique spectral signals. • Detailed spatial analysis of pen strokes using large-area ToF-SIMS imaging. This study investigates the use of time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate statistical analysis for the identification of blue pen strokes in document forgery cases. Strokes of six commercially available blue pens were analysed using ToF-SIMS, and the data were interpreted using principal component analysis (PCA) and multivariate curve resolution (MCR). PCA effectively reduced the dimensionality of the complex ToF-SIMS data, enabling clear differentiation of the blue pen inks based on the obtained PCA scores. The first three principal components accounted for most of the variance, with PCA score plots showing distinct clusters of samples corresponding to the spectra of each pen. PCA analysis of the large-area ToF-SIMS images further revealed some characteristic spatial patterns and variations across the pen strokes; however, this analysis was less useful for the identification process. On the other hand, MCR provided a more chemically intuitive analysis by extracting pure component spectra from the complex mixtures. Each MCR factor was individually associated with a specific pen, allowing for exact identification of the pen stroke made. MCR score images displayed clear correlations with individual pen strokes, and the associated loadings identified signals at particular m / z values that were characteristic of each pen. This study shows that an efficient method for non-destructive forensic analysis of blue pen strokes can be achieved by combining ToF-SIMS with PCA and MCR. This analytical method provides reliable identification, which is crucial for forgery detection without requiring any sample pretreatment, i.e. the samples (pen strokes) were used just as they were made on paper. [ABSTRACT FROM AUTHOR]

Data aggregation involves the integration of relevant data generated across platforms and devices, leveraging the potential value of sensory data. However, in addition to security and efficiency, which are the basic requirements for data aggregation involving private data, how to achieve fault tolerance and interference of aggregation in real computing networks is imminent and is the main contribution of this paper. In this paper, we propose a secure aggregation framework involving multiple servers based on coding theory, which is not only robust to clients dropping out and tolerant to partial server withdrawal but also resistant to malicious computation by servers and forgery attacks by adversaries. In particular, the proposed protocol employs the Chinese Residual Theorem (CRT) to encode private data and constructs Lagrange interpolation polynomials to perform aggregation, which achieves lightweight privacy preservation while achieving robust, verifiable and secure aggregation goals. • We propose a lightweight computational scheme for aggregation. By adopting the CRT and encoding theory, private data is encoded into polynomial coefficients for aggregation and the random data are introduced for confidential to non-collusive servers. • We propose a non-interactive verifiable security aggregation protocol that satisfies the requirements of clients dropping out and partial server corruption. Furthermore, by introducing redundant shares and utilizing results returned by delayed servers, the proposed scheme achieves verifiable computation. • We provide a thorough analysis of the robustness, verification and security of our proposed. The experimental results show that the proposed scheme is effective in both computation and verification. [ABSTRACT FROM AUTHOR]

[Display omitted] • Identification of counterfeit smartphones by conjoint ATR-FTIR and PLS-DA was proposed. • Infrared spectra from authentic and counterfeit smartphone glass screens are slightly different. • Chemometric analysis was useful to show latent information. • The multivariate model showed 100% accuracy for the discrimination of samples. • The proposed methodology is fast, cost-effective, easy, and non-destructive. Smartphone is a worldwide valuable and prevalent tool. The growing illegal fake device market is a harmful activity that needs to be fought. The lack of suitable identification methods for daily routine is one of the main obstacles in the fight against counterfeiters. In this study, the association between Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy and Partial Least Squares-Discriminant Analysis (PLS-DA) was for the first time applied to distinguish authentic and counterfeit smartphone glass screens. The sample set used to build models included the four leading brands in the Brazilian smartphone market. Principal components analysis (PCA) allowed reasonable discrimination between the two classes along the second principal component. On the other hand, the PLS-DA model showed perfect classification rates, since the sensitivity, specificity, and accuracy were equal to 100% for the validation set. Therefore, the combination of ATR-FTIR spectroscopy and PLS-DA provided a simple, low-cost, nondestructive, direct, fast, and environmentally friendly way to detect counterfeit smartphone screens in a forensic context. [ABSTRACT FROM AUTHOR]

Counterfeiting of alcoholic beverages, particularly high-value spirits such as whiskey, presents significant challenges for regulators, manufacturers, and consumers. In this study, we introduce and validate a novel application of headspace extraction (HS) followed by gas chromatography with flame ionization detection (GC-FID) for the quantitative determination of ethanol content in 42 suspected counterfeit brazilian samples of whiskeys. This method, in conjunction with visual inspection of material inconsistencies, offers a combined approach to identify potential cases of fraud. The HS-GC-FID findings revealed that only 19% of the analyzed samples had ethanol content in the limits declared on the label, emphasizing the role of ethanol content as a chemical marker for suspected beverage fraud. [Display omitted] • Headspace extraction followed by gas chromatography to examine seized whiskeys. • Ethanol content determination combined with material inspection of suspected samples. • Ethanol content is an important chemical marker of counterfeiting. [ABSTRACT FROM AUTHOR]

Digital images have numerous applications in various fields such as cyber forensics, courts, newspapers, magazines, medical field and many more. Therefore, the trustfulness of the contents of an image is very essential. Image manipulation has become very much simple and common due to the development of high resolution digital cameras, powerful computers and numerous image editing software. Hence, falsification of visual content of images is not at all limited to specialists and detection of forged images have become a highly challenging task. Of the different kinds of image forgery, the proposed method deals with one particular kind known as copy-move forgery. The method is an integration of the conventional block-based and keypoint based methods. The method first identifies the forged keypoint locations and then localizes the forged region using a region extraction technique. To improve the accuracy of detection, a Binary Discriminative Feature descriptor (BDF) is used for feature detection and matching. The suspected regions are identified by replacing the matching feature points with corresponding superpixel blocks. The neighbouring blocks are then merged with the suspected regions based on color similarity, using Color Histogram Matching and a final morphological close operation extracts the detected forged regions. Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy-move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring. [ABSTRACT FROM AUTHOR]

As a result of the increasingly pervasive deployment of the Internet of Things(IoT), the cybersecurity of IoT has already attracted more and more research efforts. Identity management is believed to be the fundamental keystone to build security mechanisms. The traditional centralized identity management scheme suffers from a single point of failure and identity forgery. A secure IoT system framework was proposed leveraging blockchain as the basic infrastructure with Physical Unclonable Functions (PUFs) identifying the sensors uniquely. We brought up a scheme to improve the identity authentication protocol. Experiments showed that our approach was more effective and secure against attacks. [ABSTRACT FROM AUTHOR]