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Fingerprint verification is a popular smartphone authentication method used even for sensitive services such as banking. However, fingerprint verification also has some issues, such as spoofing even by inexperienced impostors utilizing a thumbs up Instagram picture without the victim’s knowledge. This can be a considerable risk with partial scanning of fingertips used on smartphones. To better understand the fingerprint forgery process technical and organizational feasibility as well as user perception, we performed a hands-on forgery simulation to assess the robustness of smartphone fingerprinting technologies including a survey. Overall, 370 inexperienced participants created glue or silicone counterfeits from a photo of their fingers in a contactless way. Then they tested this physical counterfeit against smartphone contact-based fingerprint readers. Five participants logged in to smartphones with their counterfeits, and 74 registered them into smartphones as a “finger”. With improvements in the forgery process in the second run, we achieved an increase in true matches from 41 to 113 when processing scans on computer with NIST Biometric Image Software (NBIS) tool set. Our study shows that quality (measured by NFIQ) and scan enhancement is important during the forgery process – enhancement improves the match score (measured by BOZORTH3). Our analysis also provides insights into user perceptions regarding the forgery experience. Participants intend to use fingerprint authentication less often, but we found mixed results regarding the perception of fingerprint security.

To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations. PSCC-Net processes the image in a two-path procedure: a top-down path that extracts local and global features and a bottom-up path that detects whether the input image is manipulated, and estimates its manipulation masks at multiple scales, where each mask is conditioned on the previous one. Different from the conventional encoder-decoder and no-pooling structures, PSCC-Net leverages features at different scales with dense cross-connections to produce manipulation masks in a coarse-to-fine fashion. Moreover, a Spatio-Channel Correlation Module (SCCM) captures both spatial and channel-wise correlations in the bottom-up path, which endows features with holistic cues, enabling the network to cope with a wide range of manipulation attacks. Thanks to the light-weight backbone and progressive mechanism, PSCC-Net can process $1,080\text{P}$ images at 50+FPS. Extensive experiments demonstrate the superiority of PSCC-Net over the state-of-the-art methods on both detection and localization. Codes and models are available at https://github.com/proteus1991/PSCC-Net. [ABSTRACT FROM AUTHOR]

Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) are set to be the next global revolution in transportation. One of the major challenges in a CAV environment is handling the positional-forgery of Basic Safety Messages while calculating critical traffic gaps at a Stop Sign Gap Assist controlled intersection. Traditionally, positional-forgery has been handled by isolated instances of physical layer detection and correction mechanisms, which proved unreliable during the multi-hop and majority malicious vehicle scenarios. In this work, we propose a novel framework called Positional-forgEry Resistant traFfic gap Estimation for Connected inTersection management (PERFECT) that performs optimal traffic gap estimation even during multi-hop scenarios and majority malicious positional-forgery attacks. The framework reduces traffic gap estimation error during majority malicious scenarios by over 60%, keeps the average error within 8.17%–10.62% and minimizes the maximum error across a range of malicious situations. [ABSTRACT FROM AUTHOR]

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art. [ABSTRACT FROM AUTHOR]

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets. [ABSTRACT FROM AUTHOR]

Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially for copy-move forgery detection. The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue. In the first stage, atrous convolution and skip matching are incorporated to enrich spatial information and leverage hierarchical features. Spatial attention is built on self-correlation to reinforce the ability to find appearance similar regions. In the second stage, Proposal SuperGlue is proposed to remove false-alarmed regions and remedy incomplete regions. Specifically, a proposal selection strategy is designed to enclose highly suspected regions based on proposal generation and backbone score maps. Then, pairwise matching is conducted among candidate proposals by deep learning based keypoint extraction and matching, i.e., SuperPoint and SuperGlue. Integrated score map generation and refinement methods are designed to integrate results of both stages and obtain optimized results. Our two-stage framework unifies end-to-end deep matching and keypoint matching by obtaining highly suspected proposals, and opens a new gate for deep learning research in copy-move forgery detection. Experiments on publicly available datasets demonstrate the effectiveness of our two-stage framework. [ABSTRACT FROM AUTHOR]

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against some known samples, we proposed a document recapture detection scheme by employing a Siamese network to compare and extract distinct features in a recaptured document image. The proposed algorithm takes advantage of both metric learning and image forensic techniques, and forms triplets by considering some important factors in document authentication, e.g., document types, resolutions, and content in each image patch. After training with our triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushing the recaptured samples apart. In the experiment, we consider practical settings under domain differences, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed scheme consistently outperforms the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents (produced by different manufacturers, devices, and substrates), we have achieved 6.92% APCER (Attack Presentation Classification Error Rate) and 8.51% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5.00% BPCER decision threshold. [ABSTRACT FROM AUTHOR]

Advances in facial manipulation technology have led to increasing indistinguishable and realistic face swap videos, which raises growing concerns about the security risk of deepfakes in the community. Although current deepfake detectors can gain promising performance when handling high-quality faces under within-database settings, most detectors suffer from performance degradation in cross-database evaluation. Moreover, when test faces’ quality is different from training faces, the performance degrades even under within-database settings. To this end, we propose a novel Localization invariance Siamese Network (LiSiam) to enforce localization invariance against different image degradation for deepfake detection. Specifically, our Siamese network-based feature extractor takes the original image and the corresponding quality-degraded image as pairwise inputs and outputs two segmentation maps. A localization invariance loss is further proposed to impose localization consistency between the two segmentation maps. In addition, we design a Mask-guided Transformer to capture the co-occurrence between the forgery region and its surroundings. Finally, a multi-task learning strategy is utilized to obtain a robust and discriminative feature representation and jointly optimize multiple objective functions (i.e., segmentation, classification, and localization invariance losses) in an end-to-end manner. Experimental results on two public datasets, i.e., FaceForensics++ and Celeb-DF, demonstrate the superior performance of our proposed method to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society. Generally speaking, detecting manipulated faces and locating the potentially altered regions are challenging tasks. Herein, we propose a conceptually simple but effective method to efficiently detect forged faces in an image while simultaneously locating the manipulated regions. The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image. Furthermore, a noise map is estimated, playing a complementary role in capturing low-level clues and subsequently empowering decision-making. Finally, the features from these two modules are combined to distinguish fake faces. Extensive experiments show that the proposed model achieves state-of-the-art detection accuracy and remarkable localization performance. [ABSTRACT FROM AUTHOR]

Image editing techniques enable people to modify the content of an image without leaving visual traces and thus may cause serious security risks. Hence the detection and localization of these forgeries become quite necessary and challenging. Furthermore, unlike other tasks with extensive data, there is usually a lack of annotated forged images for training due to annotation difficulties. In this paper, we propose a self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a self-attention mechanism to localize forged regions in forgery images. The self-attention module is based on a Channel-Wise High Pass Filter block (CW-HPF). CW-HPF leverages inter-channel relationships of features and extracts noise features by high pass filters. Based on the CW-HPF, a self-attention mechanism, called forgery attention, is proposed to capture rich contextual dependencies of intrinsic inconsistency extracted from tampered regions. Specifically, we append two types of attention modules on top of CW-HPF respectively to model internal interdependencies in spatial dimension and external dependencies among channels. We exploit a coarse-to-fine network to enhance the noise inconsistency between original and tampered regions. More importantly, to address the issue of insufficient training data, we design a self-adversarial training strategy that expands training data dynamically to achieve more robust performance. Specifically, in each training iteration, we perform adversarial attacks against our network to generate adversarial examples and train our model on them. The proposed method is based on the assumption of content-changed manipulations. Extensive experimental results demonstrate that our proposed algorithm steadily outperforms state-of-the-art methods by a clear margin in different benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a commonality learning strategy for face video forgery detection to improve the generalization. Considering various face forgery methods could leave certain similar forgery traces in videos, we attempt to learn the common forgery features from different forgery databases, so as to achieve better generalization in the detection of unknown forgery methods. Firstly, the Specific Forgery Feature Extractors (SFFExtractors) are trained separately for each of given forgery methods. We utilize the U-net structure and consider the triplet loss, location loss, classification loss, and automatic weighted loss to ensure the detection ability of SFFExtractors on the corresponding forgery methods. Next, the Common Forgery Feature Extractor (CFFExtractor) is trained under the supervision of SFFExtractors to explore the commonality of the forgery traces caused by different forgery methods. The extracted common forgery feature is expected to have a good generalization. The experimental results on FaceForensic++ show that the SFFExtractors outperform many state-of-the-arts in face forgery detection. The generalization performance of the CFFExtractor is verified on FaceForensic++, DFDC, and CelebDF. It is proved that commonality learning can be an effective strategy to improve generalization. [ABSTRACT FROM AUTHOR]

Deep face forgery and detection is an emerging topic due to the development of GANs. Face forgery detection relies greatly on existing databases for evaluation and adequate training examples for data-hungry machine learning algorithms. However, considering the wide application of face recognition in near-infrared scenarios, there is no publicly available face forgery database that includes near-infrared modality currently. In this paper, we present an attempt at constructing a large-scale dataset for face forgery detection in the near-infrared modality and propose a new forgery detection method based on knowledge distillation named cross-modality knowledge distillation aiming to use a teacher model which is pre-trained on the visible light-based (VIS) big data to guide the student model with a small amount of near-infrared (NIR) data. The proposed near-infrared face forgery dataset, named ForgeryNIR, contains a total of over 50,000 real and fake identities. A number of perturbations are applied to help simulate real-world scenarios. All source images in ForgeryNIR are collected from CASIA NIR-VIS 2.0, and fake images are generated via multiple GAN techniques. The proposed dataset fills the gap of face forgery detection research in the near-infrared modality. A comprehensive study on six representative detection baselines is conducted to evaluate the performance of face forgery detection algorithms in the NIR domain. We further construct a hard testing set, named ForgeryNIR+, which contains forged images that have bypassed existing face forgery detection methods. The proposed datasets will be publicly available and aim to help boost further research on face forgery detection, as well as NIR face detection and recognition. [ABSTRACT FROM AUTHOR]

The increasing abuse of image editing software causes the authenticity of digital images questionable. Meanwhile, the widespread availability of online social networks (OSNs) makes them the dominant channels for transmitting forged images to report fake news, propagate rumors, etc. Unfortunately, various lossy operations, e.g., compression and resizing, adopted by OSNs impose great challenges for implementing the robust image forgery detection. To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed. Firstly, we design a baseline detector, which won the top ranking in a recent certificate forgery detection competition. Then we conduct a thorough analysis of the noise introduced by OSNs, and decouple it into two parts, i.e., predictable noise and unseen noise, which are modelled separately. The former simulates the noise introduced by the disclosed (known) operations of OSNs, while the latter is designed to not only complete the previous one, but also take into account the defects of the detector itself. We further incorporate the modelled noise into a robust training framework, significantly improving the robustness of the image forgery detector. Extensive experimental results are presented to validate the superiority of the proposed scheme compared with several state-of-the-art competitors, especially in the scenarios of detecting OSN-transmitted forgeries. Finally, to promote the future development of the image forgery detection, we build a public forgeries dataset based on four existing datasets through the uploading and downloading of four most popular OSNs. The data and code of this work are available at https://github.com/HighwayWu/ImageForensicsOSN. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks. [ABSTRACT FROM AUTHOR]

To achieve security of mobile payments within insecure public communication networks, in 2018, a secure transaction scheme for mobile payments is constructed by Yeh from a certificateless signature (CLS) scheme without bilinear pairing Yeh, (2008). However, we point out that this CLS scheme is insecure, and a malicious users can create a valid forged signature on any new message by performing public key replacement attacks. The security flaws of the underlying CLS scheme cause that the upper transaction scheme cannot keep its claimed security. In this article, we describe how this forgery attack works against the CLS scheme, which demonstrates that Yeh’s scheme cannot achieve its original security. Furthermore, to further solve the above-mentioned security flaws, a concrete construction of CLS scheme with improved security is presented. Our improved scheme can be rigorously proved using Forking lemma in the random oracle model, assuming that solving the discrete logarithm problem is infeasible. Finally, a secure transaction scheme for android-based mobile payments is proposed from our improved CLS scheme. [ABSTRACT FROM AUTHOR]

A key challenge of image splicing detection is how to localize integral tampered regions without false alarm. Although current forgery detection approaches have achieved promising performance, the integrality and false alarm are overlooked. In this paper, we argue that the insufficient use of splicing boundary is a main reason for poor accuracy. To tackle this problem, we propose an Edge-enhanced Transformer (ET) for tampered region localization. Specifically, to capture rich tampering traces, a two-branch edge-aware transformer is built to integrate the splicing edge clues into the forgery localization network, generating forgery features and edge features. Furthermore, we design a feature enhancement module to highlight the artifacts of the edge area in forgery features and assign weight values to the resulting tensor in spatial domain for vital signal strengthening and noise suppression. Extensive experimental results on CASIA v1.0, CASIA v2.0 and NC2016 demonstrate that the proposed method can accurately localize tampered regions in both pixel and edge levels and outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting, which could produce visually plausible results. Meanwhile, the malicious use of advanced image inpainting tools (e.g. removing key objects to report fake news, erasing visible copyright watermarks, etc.) has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries (not only DL-based but also traditional ones), in this work, we propose a novel end-to-end Image Inpainting Detection Network (IID-Net), to detect the inpainted regions at pixel accuracy. The proposed IID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block. Specifically, the enhancement block aims to enhance the inpainting traces by using hierarchically combined special layers. The extraction block, automatically designed by Neural Architecture Search (NAS) algorithm, is targeted to extract features for the actual inpainting detection tasks. To further optimize the extracted latent features, we integrate global and local attention modules in the decision block, where the global attention reduces the intra-class differences by measuring the similarity of global features, while the local attention strengthens the consistency of local features. Furthermore, we thoroughly study the generalizability of our IID-Net, and find that different training data could result in vastly different generalization capability. By carefully examining 10 popular inpainting methods, we identify that the IID-Net trained on only one specific deep inpainting method exhibits desirable generalizability; namely, the obtained IID-Net can accurately detect and localize inpainting manipulations for various unseen inpainting methods as well. Extensive experimental results are presented to validate the superiority of the proposed IID-Net, compared with the state-of-the-art competitors. Our results would suggest that common artifacts are shared across diverse image inpainting methods. Finally, we build a public inpainting dataset of 10K image pairs for future research in this area. [ABSTRACT FROM AUTHOR]

Counterfeit electronic devices can cause a significant revenue loss and brand value damage to the original component manufacturers (OCM). In addition, they can instigate serious safety and security issues in critical military and space applications. These devices can be injected by untrusted entities in the supply chain, such as outsourced foundries, distributors, PCB assemblers, and system integrators. Existing methods for device authenticity verification are either destructive, require an advanced electrical test or physical inspection infrastructure. Furthermore, the existing database query-based verification systems by OCMs provide an illusion of authenticity verification by looking for a device record in their online system. In reality, a customer may have bought a cloned or recycled copy of an electronic device and may find a valid record in the OCM verification system. This paper presents a blockchain-centric solution to address these limitations to verify electronic devices. A detailed study is presented to transform an existing supply chain into a trustworthy distributed ledger framework called eChain (electronic Chain). eChain generates device provenance records from blockchain that users can utilize to classify authentic and counterfeit ICs. A fully functional prototype of eChain is developed to demonstrate the feasibility and efficacy of the proposed solution. [ABSTRACT FROM AUTHOR]

We propose an antipiracy security approach for programmable analog and mixed-signal (AMS) integrated circuits (ICs). The security approach relies on functionality locking by leveraging the inherent programmability and utilizing the configuration settings as secret keys or, equivalently, the programming bits as key bits. When invalid keys are applied, the circuit is untuned and, as a result, its functionality breaks, i.e., at least one of the performances violates its specification. As long as the calibration algorithm that produces the configuration settings can be kept secret, the proposed approach can serve as a countermeasure against all types of counterfeiting, i.e., cloning, overbuilding, remarking, and recycling. An important advantage of the proposed approach is that it is lock-less. It leaves the design intact, there is no change to the design flow, and there are no performance penalty and no area or power overheads due to the lock operation. We demonstrate it on a $\Sigma \Delta $ analog-to-digital converter (ADC) with 194-bit programmability and complex calibration algorithm used in the context of highly digitized, multistandard RF receivers. [ABSTRACT FROM AUTHOR]

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%. [ABSTRACT FROM AUTHOR]

Seam carving is a representative content-aware image retargeting approach to adjust the size of an image. To preserve visually prominent content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing or duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving forgery. To attain the ability to learn low-level features, we designed a convolutional neural network (CNN) architecture comprising five types of network blocks specialized in capturing local artifacts caused by seam carving. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). The experimental results also demonstrate that our model with the ensemble module is robust for various unseen cases. [ABSTRACT FROM AUTHOR]

Counterfeit electronics entering the globalized supply chain are a growing problem impacting manufacturers and consumers alike. This article introduces Flash-DNA, a new technique for identifying the original chip manufacturer of NAND flash memory, which will help preventing the proliferation of rebranded or cloned flash memory chips. Flash-DNA relies on extracting intrinsic variations in NAND arrays using a partial block erase operation. Our experimental evaluation, conducted on multiple memory chips from four major manufacturers, shows that Flash-DNA captures fundamental properties of the manufacturing process, providing a unique identifier for a family of NAND flash memory chips that can be used for tracing their origins. [ABSTRACT FROM AUTHOR]

Detection of inconsistencies of double JPEG artifacts across different image regions is often used to detect local image manipulations, like image splicing, and to localize them. In this paper, we move one step further, proposing an end-to-end system that, in addition to detecting and localizing spliced regions, can also distinguish regions coming from different donor images. We assume that both the spliced regions and the background image have undergone a double JPEG compression, and use a local estimate of the primary quantization matrix to distinguish between spliced regions taken from different sources. To do so, we cluster the image blocks according to the estimated primary quantization matrix and refine the result by means of morphological reconstruction. The proposed method can work in a wide variety of settings including aligned and non-aligned double JPEG compression, and regardless of whether the second compression is stronger or weaker than the first one. We validated the proposed approach by means of extensive experiments showing its superior performance with respect to baseline methods working in similar conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of face manipulation technology, it is difficult for human eyes to distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) discriminators can quickly reach high accuracy in identifying fake/real face images. In this study, we explore the behavior of CNN models in distinguish fake/real faces. We find multi-scale texture difference information plays an important role in face forgery detection. Motivated by the above observation, we propose a new Multi-scale Texture Difference model coined as MTD-Net for robust face forgery detection, which leverages central difference convolution (CDC) and atrous spatial pyramid pooling (ASPP). CDC combines the pixel intensity information and the pixel gradient information to give a stationary description of texture difference information. Simultaneously, based on the ASPP, multi-scale information fusion can keep the texture features from being destroyed. Experimental results on several databases, Faceforensics++, DeeperForensics-1.0, Celeb-DF and DFDC prove that our MTD-Net outperforms existing approaches. The MTD-Net is more robust to image distortion, e.g., JPEG compression and blur, which is urgently needed in the wild world. [ABSTRACT FROM AUTHOR]

We propose a method to identify the source and target regions of a copy-move forgery so allow a correct localisation of the tampered area. First, we cast the problem into a hypothesis testing framework whose goal is to decide which region between the two nearly-duplicate regions detected by a generic copy-move detector is the original one. Then we design a multi-branch CNN architecture that solves the hypothesis testing problem by learning a set of features capable to reveal the presence of interpolation artefacts and boundary inconsistencies in the copy-moved area. The proposed architecture, trained on a synthetic dataset explicitly built for this purpose, achieves good results on copy-move forgeries from both synthetic and realistic datasets. Based on our tests, the proposed disambiguation method can reliably reveal the target region even in realistic cases where an approximate version of the copy-move localization mask is provided by a state-of-the-art copy-move detection algorithm. [ABSTRACT FROM AUTHOR]

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets. [ABSTRACT FROM AUTHOR]

Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image’s noise residual with the device’s reference PRNU is often compared with a decision threshold to check the existence of the PRNU. A PRNU correlation predictor is usually used to determine this decision threshold assuming the correlation is content-dependent. However, we found that not only the correlation is content-dependent, but it also depends on the camera sensitivity setting. Camera sensitivity, commonly known by the name of ISO speed, is an important attribute in digital photography. In this work, we will show the PRNU correlation’s dependency on ISO speed. Due to such dependency, we postulate that a correlation predictor is ISO speed-specific, i.e. reliable correlation predictions can only be made when a correlation predictor is trained with images of similar ISO speeds to the image in question. We report the experiments we conducted to validate the postulate. It is realized that in the real-world, information about the ISO speed may not be available in the metadata to facilitate the implementation of our postulate in the correlation prediction process. We hence propose a method called Content-based Inference of ISO Speeds (CINFISOS) to infer the ISO speed from the image content. [ABSTRACT FROM AUTHOR]

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation. [ABSTRACT FROM AUTHOR]

Recently, Thumbur et al. introduced a paring-free certifcateless signature scheme to resolve security and efficiency issues in resource-constrained devices and claimed it was secure. However, we find that their scheme is vulnerable to signature forgery attack. To solve the above security challenges, we present a new pairing-free certificateless signature scheme and then formally prove its security under the ECDLP assumption, and finally we conduct a performance analysis and comparison. Security analysis and performance evaluation demonstrate that our new proposal can enjoy a higher level of security with a lower computation cost and communication cost, it is more practical in resource-constrained scenarios. [ABSTRACT FROM AUTHOR]

In recent years, video surveillance has become essential for security applications used to monitor many organizations and locations, and it is therefore important to ensure the reliability of these surveillance videos. Unfortunately, surveillance videos can be forged with little effort by deleting an object from a video scene while leaving no visible traces. A fundamental challenge in video security is to determine whether or not an object has been removed from a video. This task is particularly challenging due to the lack of ground truth bases that can be used to verify the originality and integrity of video contents. In this paper, we propose a novel approach based on sequential and patch analyses to detect object removal forgery and to localize forged regions in videos. Sequential analysis is performed by modeling video sequences as stochastic processes, where changes in the parameters of these processes are used to detect a video forgery. Patch analysis is performed by modeling video sequences as a mixture model of normal and anomalous patches, with the aim to separate these patches by identifying the distribution of each patch. We localize forged regions by visualizing the movement of removed objects using anomalous patches. We conduct our experiments at both pixel and video levels to determine the effectiveness and efficiency of our approach to detection of video forgery. The experimental results show that our approach achieves excellent detection performance with low-computational complexity and leads to robust results for compressed and low-resolution videos. [ABSTRACT FROM AUTHOR]

In the implicit certification model, the process of verifying the validity of the signer's public key is combined with the verification of the signature itself. When compared to traditional, explicit certificates, the main advantage of the implicit approach lies in the shorter public key validation data. This property is particularly important in resource-constrained scenarios where public key validation is performed very often, which is common in vehicular communications (V2X) that employ pseudonym certificates. In this article, we show an alternative Schnorr-based implicit certification procedure that can improve the efficiency of a popular V2X-oriented Vehicular Public Key Infrastructure (VPKI), the Security Credential Management System (SCMS). As an additional contribution, we show that SCMS's underlying certificate provisioning procedure, based on butterfly keys, is vulnerable to existential forgery attacks under certain conditions. We then discuss how this issue can be fixed in an effective and efficient manner. [ABSTRACT FROM AUTHOR]

With recent advances in reverse engineering, attackers can reconstruct a netlist to counterfeit chips by opening the die and scanning all layers of authentic chips. This relatively easy counterfeiting is made possible by the use of the standard simple clocking scheme, where all combinational blocks function within one clock period, so that a netlist of combinational logic gates and flip-flops is sufficient to duplicate a design. In this article, we propose to invalidate the assumption that a netlist completely represents the function of a circuit with unconventional timing. With the introduced wave-pipelining (WP) paths, attackers have to capture gate and interconnect delays during reverse engineering, or to test a huge number of combinational paths to identify the WP paths. To hinder the test-based attack, we construct false paths with WP to increase the counterfeiting challenge. The experimental results confirm that WP true paths and false paths can be constructed in benchmark circuits successfully with only a negligible cost, thus thwarting the potential attack techniques. [ABSTRACT FROM AUTHOR]

In copy-move forgery, the illumination and contrast of tampered and genuine regions are highly consistent, which poses a greater challenge in copy-move forgery detection. In this article, an end-to-end neural network is proposed based on adaptive attention and residual refinement network (AR-Net). Specifically, position and channel attention features are fused by the adaptive attention mechanism to fully capture context information and enrich the representation of features. Second, deep matching is adopted to compute the self-correlation between feature maps, and atrous spatial pyramid pooling fuses the scaled correlation maps to generate the coarse mask. Finally, the coarse mask is optimized through the residual refinement module, which retains the structure of object boundaries. Extensive experiments, evaluated on CASIAII, COVERAGE, and CoMoFoD datasets, demonstrate that the AR-Net has superior performance than state-of-the-art algorithms and can locate tampered and corresponding genuine regions at the pixel level. In addition, AR-Net has high robustness on postprocessing operations, such as noise, blur, and JPEG recompression. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are progressive ad hoc networks that comprise of distributed sensors that are typically and randomly deployed over the target region. The valuable information from the sensor nodes is allowed to access with the help of gateway node by the registered user. To ensure secure communication, a session key is exchanged between the participants over the insecure channel. In this paper, we identified some deficiencies in Jung et al.'s scheme and then devised an enhanced biometric-based anonymous user authentication and the key agreement scheme that is also embedded with symmetric cryptosystem for WSNs. The advantage of using biometric login is to ensure the legal user's efficient login. We conferred about preserving the security of our proposed scheme, we primarily applied formal verification BAN-logic method to check the exactness of mutual authentication. Furthermore, used automated validation of internet security protocols and applications software that is widely accepted and its results confirm that our scheme is secure against active and passive attacks, including forgery, replay, and man-in-the-middle attack. In addition, an informal analysis proves our scheme can withstand various possible attacks on authentication protocols over the insecure channel. Furthermore, our scheme is more appropriate for WSNs based upon the comparison of computational efficiency and security requirements with recent results.

Provable Data Possession is viewed as an important technique to check the integrity of the data stored in remote servers. Recently, a new provable data possession scheme [Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage, IEEE Transactions on Services Computing, (2018) Doi: 10.1109/TSC.2018.2820713] was proposed. The authors claimed this scheme can guarantee the storage correction. In this paper, we show this scheme cannot satisfy this fundamental security. Specifically, we demonstrate the malicious cloud can generate a proof to pass the third party auditor's verification even if it does not store the user's whole file. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on new solutions and research initiatives in the area of media authentication and forensics. Media manipulation is now a pressing societal problem with broad implications. In the past, it required significant skill to create compelling manipulations because editing tools, such as Adobe Photoshop, required experienced users to alter images convincingly. Over the last several years, the rise of machine learning-based technologies has dramatically lowered the skill necessary to create compelling manipulations. For example, Generative Adversarial Networks (GANs) can create photo-realistic faces with no skill by an end-user other than the ability to refresh a web page. Deepfake algorithms, such as autoencoders to swap faces in a video, can create manipulations much more easily than previous generation video tools. While these ML techniques have many positive uses, they have also been misused for darker purposes such as to perpetrate fraud, to create false personas, and to attack personal reputations. Clearly, these are pressing and important problems for which technical solutions must play a role. [ABSTRACT FROM AUTHOR]

In this paper, we propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called “community structure” in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure. [ABSTRACT FROM AUTHOR]

Deepfakes, or artificially generated audiovisual renderings, can be used to defame a public figure or influence public opinion. With the recent discovery of generative adversarial networks, an attacker using a normal desktop computer fitted with an off-the-shelf graphics processing unit can make renditions realistic enough to easily fool a human observer. Detecting deepfakes is thus becoming important for reporters, social media platforms, and the general public. In this work, we introduce simple, yet surprisingly efficient digital forensic methods for audio spoof and visual deepfake detection. Our methods combine convolutional latent representations with bidirectional recurrent structures and entropy-based cost functions. The latent representations for both audio and video are carefully chosen to extract semantically rich information from the recordings. By feeding these into a recurrent framework, we can detect both spatial and temporal signatures of deepfake renditions. The entropy-based cost functions work well in isolation as well as in context with traditional cost functions. We demonstrate our methods on the FaceForensics++ and Celeb-DF video datasets and the ASVSpoof 2019 Logical Access audio datasets, achieving new benchmarks in all categories. We also perform extensive studies to demonstrate generalization to new domains and gain further insight into the effectiveness of the new architectures. [ABSTRACT FROM AUTHOR]

A novel image copy-move forgery detection scheme using a Dense-InceptionNet is proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense-feature connection, Deep Neural Network (DNN). It is the first DNN model to autonomously learn the feature correlations and search the possible forgery snippets through the matching clues. The proposed Dense-InceptionNet consists of Pyramid Feature Extractor (PFE), Feature Correlation Matching (FCM), and Hierarchical Post-Processing (HPP) modules. The PFE module is proposed to extract multi-dimensional and multi-scale dense-features. The features of each layer in this extractor module are directly connected to the preceding layers. The FCM module is proposed to learn the high correlations of deep features and obtain three candidate matching maps. Finally, the HPP module which makes use of three matching maps to obtain a combination of cross-entropies is amenable to better training via backpropagation. Experiments demonstrate that the efficiency of the proposed Dense-InceptionNet is much better than the other state-of-the-art methods while achieving the relative best performance against most known attacks. [ABSTRACT FROM AUTHOR]

Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user’s signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures). [ABSTRACT FROM AUTHOR]

In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes. [ABSTRACT FROM AUTHOR]

With the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 ~ 360°) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF. [ABSTRACT FROM AUTHOR]

We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. [ABSTRACT FROM AUTHOR]

Science museums have a long tradition in resorting to reproductions, both to preserve original artifacts and to make exhibitions attractive by presenting the material culture of science and technology. The art of making copies of precious objects, sometimes for fakery or forgery, is as old as collecting, but since the 19th century it has become both commonplace and profitable. In this article, the authors reconstruct the life and works of a 20th century model maker, Roberto Guatelli, whose reproductions of technological artifacts of the past, particularly calculators, can be found in several museums. [ABSTRACT FROM AUTHOR]

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications. [ABSTRACT FROM AUTHOR]

Electronics supply chain vulnerabilities have broadened in scope over the past two decades. With nearly all integrated circuit (IC) design companies relinquishing their fabrication, packaging, and test facilities, they are forced to rely upon companies from around the world to produce their ICs. This dependence leaves the electronics supply chain open to counterfeiting activities. In this article, we propose an electromagnetic (EM)-based fingerprinting framework, called EMFORCED, to detect remarked and cloned counterfeit ICs. Here, we demonstrate the benefits of using naturally occurring EM side channels to identify the IC design layout without decapsulating the chip under test. Enabling only the clock, $V_{\mathrm{ dd}}$ , and ground pins allows us to generate a design-specific fingerprint that is dependent upon the physical parameters of the chip under test. EMFORCED leverages the EM emissions from the clock distribution network to create a holistic, design-level, fingerprint, including both temporal information and spatial information. We utilize the fingerprint information of functionally similar 8051-series microprocessors from three vendors and perform unsupervised (principal component analysis) and supervised (linear discriminant analysis) machine learning methods on all ICs to determine their intravendor and intervendor similarities. We acquired ICs from multiple dates and lot codes along with variants acquired from the gray market and analyzed them for authenticity using physical inspection and X-ray tomography. Statistical analysis and machine learning techniques are used to demonstrate the reference-free and reference-inclusive classification methods based on EMFORCED measurements. We demonstrate the classification accuracies of 99.46% and 100% for unsupervised and supervised approaches, respectively. [ABSTRACT FROM AUTHOR]