Search

For Deepfake detection, many existing works use the cross-entropy loss to enforce the classifier network to learn the mapping relationship from the RGB domain to the class domain, lacking an explicit constraint to guide the feature extraction network to learn discriminative features from an input image. This constrains the feature representation capability to expose deepfake. In this work, we analyze the feature extraction network in terms of both difference and similarity capabilities and propose a new constraint called similarity loss (SL) to improve the detection performance of the convolutional neural network (CNN) based detector. Moreover, according to the experimental results of the SL on data augmentation effectiveness, we propose a simple yet efficient framework, which is called as efficient similarity representation learning (ESRL), for deepfake detection. Extensive experiments on three public datasets (namely FF++, DFDC, and Celeb-DF) show that the feature extraction network trained with the help of SL can map forged faces and real faces to different feature embedding and map the same type of forged faces to similar feature embedding. [ABSTRACT FROM AUTHOR]

The distributed authentication of a large number of resource-constrained power terminal devices (PTDs) is crucial for ensuring the security of the power IoT communication. However, existing solutions are inadequate in terms of security (such as vulnerability to single point failures or insider attacks) and communication costs, which do not meet the requirements of the power IoT environment. Therefore, we propose a zero-trust-based mutual authentication scheme in cloud-edge-end collaboration power IoT environment based on blockchain technology. The proposed solution involves deploying a distributed multi-point zero-trust engine around dense PTDs to collect real-time identity information. Through the maintenance of an alliance blockchain, the edge server securely shares and traces identity information, preventing tampering and effectively blocking threats related to lost terminals during authentication. Additionally, a lightweight ECC key management scheme ensures the security of authentication information. The proposed scheme effectively defends against common attacks such as replay attacks and identity forgery attacks through informal security analysis, while its security is evaluated using the Canetti and Krawczyk (CK) adversary models. Performance evaluation results demonstrate that the proposed scheme achieves effectiveness without the compromising required security attributes, which obtains up to 58% improvement 58% improvement in computation delay and a 56.9% improvement in communication costs over previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Frame deletion detection is a challenging task in the field of digital video forensics. This paper proposes a deep-learning-based frame deletion detection method for single-shot videos. We capture traces of frame deletion forgery from both adjacent and long-range continuous frames. Specifically, we propose a novel multi-scale difference convolutional neural network (MSDCNN) structure, which models different levels of inter-frame variations. Then, we use the long-short-term memory network (LSTM) to capture the long-term variation pattern of multi-scale differential features. The proposed method is a simple and principled frame deletion detection framework with a small computational cost. According to the experiments, the proposed framework can achieve a more advanced performance of frame deletion detection than traditional methods and methods based on 3D convolutions. [ABSTRACT FROM AUTHOR]

Metaphysicians who are aware of modern physics usually follow Putnam (1967) in arguing that Special Theory of Relativity is incompatible with the view that what exists is only what exists now or presently. Partisans of presentism (the motto 'only present things exist') had very difficult times since, and no presentist theory of time seems to have been able to satisfactorily counter the objection raised from Special Relativity. One of the strategies offered to the presentist consists in relativizing existence to inertial frames. This unfashionable strategy has been accused of counterfeiting, since the meaning of the concept of existence would be incompatible with its relativization. Therefore, existence could only be relativistically invariant. In this paper, I shall examine whether such an accusation hits its target, and I will do this by examining whether the different criteria of existence that have been suggested by the Philosophical Tradition from Plato onwards imply that existence cannot be relativized. [ABSTRACT FROM AUTHOR]

The detection of copy-move forgeries has been of utmost relevance in the field of digital image forensics because of the explosive growth of image altering tools. The paper provides a thorough overview of current developments in copy-move forgery detection methods. Block-based, keypoints-based, and deep learning-based methods represent the three distinct categories into which the methodologies in the survey are divided. The papers in each category are thoroughly analysed, taking into consideration important factors including pre-processing techniques, feature extraction strategies, feature matching methods, and performance evaluation using various metrics and datasets. This survey study provides a thorough overview of the state of the field by methodically synthesizing and assessing the surveyed papers, and it also offers helpful insights for researchers and practitioners working to improve the accuracy and robustness of copy–move forgery detection methods in digital image forensics. [ABSTRACT FROM AUTHOR]

The real threat to the privacy of a plain document exchanged over insecure channels is content manipulation or eavesdropping by unauthorized parties. To protect a transferred document, the architecture proposed in this paper offers encryption, authentication, and scrambling GAN with shuffle confusion (EAGAN) comprehensively with a relatively rapid execution time average of 2s for each colored document. The EAGAN encrypts and signs the document content at the origin point (sender side) and then decrypts and verifies the cipher document at the receiver side, achieving two verification levels and three levels of confidentiality, reinforced with two keys of chaos and equal document content. Each document has a unique hash value (signature or identity) with a QR code watermark to detect forgery, even when the change is small (even by one bit), without depending on any third party. If case the intermediate encrypted document, the neural network model (Decoder-Key), the cipher document, or the initial values of the Chaos Key are leaked, an unauthorized person won't be able to retrieve the document. As proven by the experiments in this paper, EAGAN has characteristics that make it more resistant to security threats. [ABSTRACT FROM AUTHOR]

Sanctity and integrity of digital videos are crucial for the diverse real-world applications. It has significant social and legal implications. The technological advancements are posing new challenges as the video processing software that are typically designed to enhance the visual content, can adversely spawn unauthentic and malicious data that can be potentially hazardous. Robust algorithms are therefore needed to counter the deleterious effects. In this paper, we propose a passive-blind approach to detect and localize multiple kinds of inter-frame forgeries in digital videos like frame insertion, deletion and duplication. The forensic artefacts are designed based on correlation inconsistencies between the histogram-similarity patterns of the adjacent texture-feature encoded video frames. For the empirical evaluation, the algorithm uses texture features such as Histogram of Oriented Gradients (HoG), uniform and rotation invariant Local Binary Pattern (LBP). A customized dataset of 1370 tampered videos is created using the benchmark SULFA dataset due to lack of standard video dataset with inter-frame forgeries. A supervised SVM classifier is trained to detect video tampering where extensive analysis based on different histogram-similarity metrics is carried out with the proposed approach that exhibits an overall accuracy 99%. Further, the proposed method localizes the position of tampered frames in the video. It highlights forged frames using Chebyshev's inequality in case of frame insertion and deletion attacks. A comparative analysis with state-of-the-art methods is also presented that exhibits good efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

In the current digital era, images have become one of the key channels for communication and information. There are multiple platforms where digital images are used as an essential identity, like social media platforms, chat applications, electronic and print media, medical science, forensics and criminal investigation, the court of law, and many more. Alternation of digital images becomes easy because multiple image editing software applications are accessible freely on the internet. These modified images can create severe problems in the field where the correctness of the image is essential. In such situations, the authenticity of the digital images from the bare eye is almost impossible. To prove the validity of the digital images, we have only one option: Digital Image Forensics (DIF). This study reviewed various image forgery and image forgery detection methods based on blind forgery detection techniques mainly. We describe the essential components of these approaches, as well as the datasets used to train and verify them. Performance analysis of these methods on various metrics is also discussed here. [ABSTRACT FROM AUTHOR]

Recently, a novel semi-quantum ring signature protocol (Quantum Inf. Process., 22(9), 337(2023)) was proposed. Although it has the properties of semi-quantum protocol, its verification process will fail when more than two users take part in the signature protocol. We show an example, in which four participants including the trusted third-party Trent are used, to prove the signature cannot be verified. Then, an improved semi-quantum ring signature protocol is proposed. In the new ring signature protocol, Trent generates the shared keys with each ring user. According to the shared keys, Trent encodes the GHZ state such that the identity of the true ring signatory can be traced by Trent. What is more, in our protocol, by measuring the received particles, all the ring users can create the same session key to generate a ring signature, which can guarantee the anonymity of the voter in the protocol. The improved ring signature protocol overcomes the drawback of the old one and can be secure against various eavesdropping attacks and forgery attack. Compared with the similar quantum ring signature protocols, ours has better practicability and efficiency. [ABSTRACT FROM AUTHOR]

One of the most pressing challenges in audio forgery detection—a major topic of signal analysis and digital forensics research—is detecting copy-move forgery in audio data. Because audio data are used in numerous sectors, including security, but increasingly tampered with and manipulated, studies dedicated to detecting forgery and verifying voice data have intensified in recent years. In our study, 2189 fake audio files were produced from 2189 audio recordings on the TIMIT corpus, for a total of 4378 audio files. After the 4378 files were preprocessed to detect silent and unsilent regions in the signals, a Mel-frequency-based hybrid feature data set was obtained from the 4378 files. Next, RNN and LSTM deep learning models were applied to detect audio forgery in the data set in four experimental setups—two with RNN and two with LSTM—using the AdaGrad and AdaDelta optimizer algorithms to identify the optimum solution in the unlinear systems and minimize the loss rate. When the experimental results were compared, the accuracy rate of detecting forgery in the hybrid feature data was 76.03%, and the hybrid model, in which the features are used together, demonstrated high accuracy even with small batch sizes. This article thus reports the first-ever use of RNN and LSTM deep learning models to detect audio copy-move forgery. Moreover, because the proposed method does not require adjusting threshold values, the resulting system is more robust than other systems described in the literature. [ABSTRACT FROM AUTHOR]

At present, in addressing security challenges within 5 G-era Vehicular Ad-Hoc Networks (VANET), a new protocol has been designed in this paper, named V2X-GKA (Dynamic Group Authentication and Key Agreement Protocol based on C-V2X). V2X-GKA utilizes cryptographic techniques such as ECDLP and DBDH to effectively mitigate risks associated with certificate forgery and key theft. Through the integration of authentication and group key agreement mechanisms, the protocol facilitates dynamic member management and secure key updates without necessitating complete protocol re-execution. This approach not only ensures both forward and backward security but also optimizes algorithmic processes, rendering it highly suitable for real-time, high-speed mobile environments. Through necessary security analysis, it is confirmed that the V2X-GKA scheme in this paper meets the existing security objectives. Compared with other similar schemes, it exhibits a certain degree of completeness and superiority, particularly in open-channel scenarios. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in the field of face forgery and face forgery detection. However, the performance of the detection methods in the unknown environment is far beyond satisfactory due to the feature distribution deviation of different fake face generators. In this paper, we adopt the domain generalization theory to improve the generality of fake face detection. The utilized method augments the original image samples by introducing gradient noise yielded during back-propagation, simulating the forgery features in unknown domains. In the construction of the detection network, we propose a multi-scale synthetic artifact trace tracker (MSATT) to enhance the manipulation traces through multi-scale content suppression. Meanwhile, we observed that the synthesized images present a noticeable color abnormality after going through the proposed MSATT module. Therefore, we designed a color difference perception network (CDPNet) to capture this unique feature. Experimental results demonstrate that both the domain augmentation and the proposed CDPNet can effectively improve the performance of the detection network. The proposed method is competitive with the state-of-the-art face forgery detection methods on both intra- and inter-dataset evaluations. [ABSTRACT FROM AUTHOR]

The article considers the issues of the secure operation of a two-level blockchain with a complex mixed consensus protocol, i.e., Proof-of-Stake in the main blockchain (mainchain) and Proof-of-Work in the secondary one (sidechain). This two-level blockchain is constructed on the principle of the Proof-of-Proof protocol, where the safety of the sidechain is ensured by the stability of the mainchain, by referring the mainchain blocks to the sidechain blocks using special transactions. Such a structure allows faster block creation in the sidechain and, accordingly, faster processing of transactions without the loss of security and without increasing the volume of the block. Such a two-level blockchain is of the greatest interest for the creation of a cascade system of state registers, which guarantees the security against the substitution and forgery of documents. The main results of the article are explicit analytical expressions for estimates of probability of double spend attack on a sidechain in a two-level blockchain, under the condition of presence of an adversary in the sidechain and the mainchain. [ABSTRACT FROM AUTHOR]

Recent works have demonstrated the great performance of forgery image forensics based on deep learning, but there is still a risk that detectors could be susceptible to unknown illegal attacks, raising growing security concerns. This paper starts from the perspective of reverse forensics and explores the vulnerabilities of current image manipulation detectors to achieve targeted attacks. We present a novel reverse decision aggregate gradient attack under low-frequency constraints (RevAggAL). Specifically, we first propose a novel pixel reverse content decision-making (PRevCDm) loss to optimize perturbation generation with a specific principle more suitable for segmenting manipulated regions. Then, we introduce the low-frequency component to constrain the perturbation into more imperceptible details, significantly avoiding the degradation of image quality. We also consider aggregating gradients on model-agnostic features to enhance the transferability of adversarial examples in black-box scenarios. We evaluate the effectiveness of our method on three representative detectors (ResFCN, MVSSNet, and OSN) with five widely used forgery datasets (COVERAGE, COLUMBIA, CASIA1, NIST 2016, and Realistic Tampering). Experimental results show that our method improves the attack success rate (ASR) while ensuring better image quality. [ABSTRACT FROM AUTHOR]

Certificateless Proxy Signature (CLPS) offers a comprehensive authentication mechanism to ensure the optimal solutions from modern forgery attacks in Industrial Internet of Things (IIoTs) environment. CLPS is part of certificateless public key cryptography (CL-PKC) and has the benefits of eradicating many issues like key distribution problem. Although costly computational processing capabilities are consumed by smart devices during authentication process. This article proposed an authentication scheme for CLPS by using the mathematical cryptographic curve concepts of Hyper Elliptic Curve (HEC) to provide a secure and optimized communication approach in IIoTs environment. The arises key distribution problem in CLPS is also fixed. The scrutinized security analysis of proxy signature and delegation signature were performed to tackle different attacks like Machine-in-the-middle Attack (MiTM), Replay Attack, Key Replacement Attack, Impersonation Attack, and Chosen Message Attack. Due to HEC-DLP the forgery attack cannot succeeded. Finally, the proposed work is formally verified through the AVISPA tool. The comprehensive performance evaluation shows that the total computational cost is reduced to 49.48% and the communication overhead size is reduced to 49.57% with a comparative analysis of existing schemes. Thus, the proposed approach eradicates key distribution problems with an equal security level of RSA and ECC. [ABSTRACT FROM AUTHOR]

Recommender systems are vulnerable to attacks because of their open nature. Counterfeit users give biased ratings to the items due to various objectives that may lead to the loss of user trust. The attackers use certain attack models with specific features. The existing attack detection techniques are typically attack-specific and work only when the attack features are known. They are unable to identify an unknown attack with unfamiliar features. To diminish this problem, in this paper, we propose a generalized solution that filters any attack irrespective of its design and features. We trained the classifiers with the ratings of the known authentic users using one-class SVM and PU learning models for detecting attacks, considering their ability to detect anomalies in the dataset caused by unknown attacks. The openly available MovieLens dataset has been used to assess our designed attack detection method. The experimental results show that all unknown attacks are successfully detected with 100% accuracy. The same detection accuracy is achieved for attacks with known features. [ABSTRACT FROM AUTHOR]

In the contemporary digital era, images are omnipresent, serving as pivotal entities in conveying information, authenticating experiences, and substantiating facts. The ubiquity of image editing tools has precipitated a surge in image forgeries, notably through copy-move attacks where a portion of an image is copied and pasted within the same image to concoct deceptive narratives. This phenomenon is particularly perturbing considering the pivotal role images play in legal, journalistic, and scientific domains, necessitating robust forgery detection mechanisms to uphold image integrity and veracity. While advancements in Convolutional Neural Networks (CNN) have propelled copy-move forgery detection, existing methodologies grapple with limitations concerning the detection efficacy amidst complex manipulations and varied dataset characteristics. Additionally, a palpable void exists in comprehensively understanding and exploiting dataset heterogeneity to enhance detection capabilities. This heralds a pronounced exigency for innovative CNN architectures and nuanced understandings of dataset intricacies to augment detection capabilities, which has remained notably underexplored in the prevailing literature. Against this backdrop, our research broaches novel frontiers in copy-move forgery detection by introducing an innovative CNN architecture meticulously tailored to discern the subtlest manipulations, even amidst intricate image contexts. An extensive analysis of multiple datasets – MICC-F220, MICC-F600, and a combined variant – enables us to delineate a granular understanding of their attributes, thereby shedding unprecedented light on their influences on detection performance. Further, our research goes beyond mere detection, delving deep into comprehensive analyses of varied datasets and conducting additional experiments with differential training-validation sets and randomly labeled data to scrutinize the robustness and reliability of our model. We not only meticulously document and analyze our findings but also juxtapose them against extant models, offering an exhaustive comparative analysis. [ABSTRACT FROM AUTHOR]

The identification of the tampered images is the research area of digital forensics, which defines the manipulation in the image. This is easily performed by forgers without the permission of the owners of the images. The misuse of these images in digital content is a significant problem for privacy. There are some studies on the detection of the forgery images, in the literature. However, effective and robust solutions are needed to detect them. In this motivation, a deep learning based architecture is proposed to solve the tampered image detection. It includes feature extraction with transfer learning architecture, selection of features with particle swarm optimization, and multi-classification of images with a deep learning architecture that is designed with Gated Recurrent Units. The proposed architecture is validated with the modified CASIA dataset. Various noisy tampered images are also included in the dataset of the study to demonstrate the robustness of the method. Despite this, tampered and noisy tampered images can even be detected quite accurately. As a result of the experiments, 96.25% accuracy is achieved with the proposed method. An accuracy of 80.5% was achieved in images with Gaussian and salt & pepper noises together. It has been proven through experiments that these results are significantly higher than the SVM classifier. This achievement is capable of supporting the detection of the original image combined with different images by experts working in the field of digital forensics. [ABSTRACT FROM AUTHOR]

The common approach in secure communication channel protocols is to rely on ciphertexts arriving in-order and to close the connection upon any rogue ciphertext. Cryptographic security models for channels generally reflect such design. This is reasonable when running atop lower-level transport protocols like TCP ensuring in-order delivery, as for example, is the case with TLS or SSH. However, protocols like QUIC or DTLS which run over a non-reliable transport such as UDP, do not—and in fact cannot—close the connection if packets are lost or arrive in a different order. Those protocols instead have to carefully catch effects arising naturally in unreliable networks, usually by using a sliding-window technique where ciphertexts can be decrypted correctly as long as they are not misplaced too far. In order to be able to capture QUIC and the newest DTLS version 1.3, we introduce a generalized notion of robustness of cryptographic channels. This property can capture unreliable network behavior and guarantees that adversarial tampering cannot hinder ciphertexts that can be decrypted correctly from being accepted. We show that robustness is orthogonal to the common notion of integrity for channels, but together with integrity and chosen-plaintext security it provides a robust analog of chosen-ciphertext security of channels. In contrast to prior work, robustness allows us to study packet encryption in the record layer protocols of QUIC and of DTLS 1.3 and the novel sliding-window techniques both protocols employ. We show that both protocols achieve robust chosen-ciphertext security based on certain properties of their sliding-window techniques and the underlying AEAD schemes. Notably, the robustness needed in handling unreliable network messages requires both record layer protocols to tolerate repeated adversarial forgery attempts. This means we can only establish non-tight security bounds (in terms of AEAD integrity), a security degradation that was missed in earlier protocol drafts. Our bounds led the responsible IETF working groups to introduce concrete forgery limits for both protocols and the IRTF CFRG to consider AEAD usage limits more broadly. [ABSTRACT FROM AUTHOR]

Face anti-spoofing, which aims to defend against various attacks in face authentication systems, has drawn increasing attention with the advance in biometrics. Although many studies for face anti-spoofing have shown the remarkable performance improvement, they still suffer from a lack of generality, i.e., vulnerability to unknown attacks frequently occurring in real-world scenarios. Recently, the one-class learning approach has emerged as a way to overcome the aforementioned issue thanks to its capability to distinguish unseen forgery types by precisely understanding the subtle pattern of real faces. However, it is quite a difficult problem to determine the accurate decision boundary by only using real facial images. To cope with this limitation, we propose to apply a novel pseudo-negative sampling scheme in one-class learning for face anti-spoofing. More concretely, pseudo-negative samples are generated based on the statistical distribution of real facial samples and utilized as the proxy of fake facial samples to construct the robust decision boundary. The proposed method is designed by following a two-stage unsupervised learning framework. Firstly, the model learns the feature representation of real faces via the Siamese architecture in the pre-training stage. In the fine-tuning stage, pseudo-negative features are randomly sampled at a suitable distance from real facial features in the latent space. These sampled features are then utilized with real facial features to guide the classifier. Since such pseudo-negative features are not limited to specific fake properties, our classifier can effectively learn to distinguish real and fake faces without using any fake facial images during the training. Experimental results on benchmark datasets show that the proposed method is effective for face anti-spoofing even with unseen spoofing attacks, which achieves the state-of-the-art performance on the Replay-Attack dataset, i.e., 94.48% in AUC. The code and model are publicly available at: https://github.com/DCVL-FA/PNS-release. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have achieved impressive successes in fake face image detection. However, CNNs ignore tampering traces outside their attention scope. Moreover, example forgetting events can also pose negative impacts on the face forgery detection accuracy. To address these issues, this paper proposes an attention-expanded two-stage face forgery detector, named Attention-expanded Deepfake Spotter (ADS). In the first stage, the manipulated regions are preliminarily located by utilizing the Region Score Maps (RSMs) generated by the modified CNN. In the second stage, the Expanding and Undetectable Regions (EUR) loss function is designed to encourage another modified CNN to mine manipulation traces outside the manipulated areas exposed in the first stage. To fuse the manipulation traces extracted from different regions in the two stages and mitigate the problems caused by example forgetting events, RSM-weighted accumulation is adopted to integrate the detection information from both stages and obtain the final detection result. The proposed algorithm's effectiveness for each component is analyzed through ablation experiments, and the method is evaluated on four publicly available datasets: FF++, HFF, DFDC, and Celeb-DF. The experimental results demonstrate that the proposed method has high detection rates and superior transferability, outperforming most existing algorithms. [ABSTRACT FROM AUTHOR]

GANs (Generative Adversarial Networks) are widely employed in many domains of science and technology. They have produced high-resolution, photo-realistic faces that appear real to the human eye. However, recognizing these images is becoming increasingly difficult. This study introduces a new GAN ensemble model that is intended to improve GAN training issues (Mode Collapse and Convergence) and generate knowledge from diverse input samples. The proposed model is built using multiple CNN discriminators architecture based on the voting ensemble technique. It utilizes a modified diversity loss function designed with the goal of minimizing the distance between the generated and original distributions. It is proven as a robust technique for forgery detection, achieving 98.31% accuracy values. One of the major findings of the study is that the novel method outperforms existing GAN models based on a small dataset of "Face Mask Lite"(193 Unmasked images), using quantifiable parameters such as Inception score (IS), Fréchet Inception distance (FID), SSIM (Structural Similarity Index Metric) and Total Computational Time Function. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the detection of copy-move image forgery, a commonly employed technique for image manipulation. The proposed method combines a modified version of the Gabor Filter and Centre Symmetric Local Binary Pattern (CS-LBP) for feature extraction, aiming to meet the growing demand for accurate forgery detection. The process involves pre-processing the image and extracting features using the Gabor filter and CS-LBP at varying scales and orientations. Key points are matched using the Manhattan distance to identify forged regions. Classification of the forged images is achieved using Hybrid Neural Networks with Decision Tree (HNN-DT). In order to assess the performance of the presented method, diverse image datasets are used and compared to existing feature extraction techniques. The results demonstrate the effectiveness of the modified Gabor filter with CS-LBP in accurately classifying forged images. Specifically, the HNN-DT method with Gabor filter CS-LBP feature extraction surpasses HNN-DT with SURF and PCA feature extraction in terms of classification accuracy and overall performance. Evaluation on the CoMoFoD database confirms superior results compared to existing techniques, establishing the proposed method as a reliable approach for distinguishing between authentic and forged images. Consequently, it serves as a robust solution for image classification and forgery detection. The presented work focuses on detecting mainly three types of image forgery, namely retouching, splicing, and cloning. It is applicable to various image formats, including JPEG and BMP, and is designed specifically for forensic applications in image forgery detection. [ABSTRACT FROM AUTHOR]

The copy-move forgery technique is the most commonly exercised among different image tampering techniques. In this approach, some part of an image is copied and pasted into the different locations of the same image. In this paper authors suggested a block-based copy move forgery detection technique utilizing SWT-SVD features. First, the shift-invariant approximation band has been obtained by using the SWT technique. In the next step, the obtained approximate band has been decomposed by block-based SVD. In the proposed scheme, the obtained SVD plane is represented as a multiresolution plane, and further statistical features are derived from those planes. Thus, an elegant copy-move forgery detection technique is introduced by considering significantly small feature vectors. This method has been tested on CoMoFoD dataset images and exhibits desired results compared with other state-of-the-art copy-move forgery detection methods in spite of having a lesser feature vector size. This method works effectively even under various post-processing attacks showing its applicability in a real-life scenario. [ABSTRACT FROM AUTHOR]

The implicit moral decision exists widely in our daily life, such as the non-deceptive counterfeit luxury consumption, but its moral decision mechanism remains to be clarified. This study is dedicated to studying how moral beliefs affect individuals' moral decision process and decision outcomes in implicit moral decisions, as well as the effect of attitude function, which can impact the implicit degree of moral decision. We employed transcranial direct current stimulation (tDCS) to examine neural underlying and effect of moral belief on counterfeit luxury consumption. In this study, we adopted 2 (stimulation group: active vs. sham, between-subject) × 2 (attitude function: social-adjustive vs. value-expressive, within-subject) mixed design. 58 participants were randomly assigned to either active or sham tDCS groups (a-tDCS vs. s-tDCS). Our results showed that the moral belief of counterfeit luxury consumption was higher for a-tDCS group (left anodal/right cathodal tDCS over dorsolateral prefrontal cortex) than s-tDCS group (sham stimulation). The counterfeit luxury purchase intention of s-tDCS group is higher. Moreover, we also found reaction time of value-expressive function is longer for a-tDCS group. Hence, these results suggested that moral beliefs can change implicit moral decisions, which is also related to left dorsolateral prefrontal cortex (DLPFC). Besides, attitude functions can affect the relationship between moral beliefs and moral decision processes. In summary, the current study's research findings can help understand and solve the social problems caused by implicit moral decisions. [ABSTRACT FROM AUTHOR]

Image forgery detection has become a hot research topic in security and forensics applications. Keypoints (KPs) based image forgery detection extracts image KPs and utilizes local visual features for identifying forgery regions which shows excellent performance on the basis of memory and robustness over attacks. But, these methods are lacking in handling the cases when forgeries in small regions, in which KPs are limited. To address these challenges, this work introduces a fast and efficient forgery detection using the processes like pre-processing, feature extraction and optimal classification. Initially, the RGB image is converted into L*a*b color space. Then, the KPs are determined using the Eigenvalue Asymmetry (EAS) and after determining the KPs, the texture features like Haralick and skewness are extracted. Then, these features are classified by the deep learning (DL) classifier Deep belief network (DBN) with an adaptive crow search algorithm (ACSA). The proposed DBN-ACSA classifier categorizes the detected image as a normal or forgery image. The experimental results are carried out on the Vision, UCID and CoMoFoD datasets and achieved accuracy, precision, F1 score, recall and ROC values. [ABSTRACT FROM AUTHOR]

Baijiu is a unique distilled spirit in China. The bulk Baijiu market has been experiencing issues related to counterfeit and substandard products, raising concerns about food safety. Detecting liquor adulteration is crucial for eliminating fraud in the bulk Baijiu market. In this study, we proposed using fluorescence hyperspectral Technology (FH) combined with machine learning (ML) to detect Baijiu adulteration quickly and non-destructive. Due to the similarity of fluorescence spectral features between adulterated Baijiu and real Baijiu, it was difficult to distinguish them based on the fluorescence feature parameters alone. The data preprocessing methods were used and then principal component analysis (PCA) was adapted. The principal components were used as inputs to ML models to establish the qualitative and quantitative detection models. In the qualitative detection models, the Adaptive Boosting (AdaBoost) model demonstrated the best performance with 98.08% precision, 100% recall and 99.03% F1-score. In the quantitative detection models of adulterations concentration, the AdaBoost model after Wavelet denoising(WDS) processing yielded the best results with R2 of 0.9740 and RMSEP of 0.0247. The results demonstrated that the combination of FH and ML can efficiently detect adulterated bulk Baijiu, showing promising applications and feasibility in the nondestructive detection of adulterated substances. [ABSTRACT FROM AUTHOR]

The active ingredient group is a prominent feature reflecting the inherent characteristics of plant-based functional foods. Chinese hawthorn leaf (CHL), a tea substitute possessing intrinsic nutritional properties in anti-hyperlipidemia, was first found to be adulterated with Malus doumeri leaf (MDL) owing to similar commercial labels. In this context, the above-mentioned two contrasting species were explored through phytochemical profiling and activity assessment. The amelioration effect of CHL on free fatty acids-elicited lipid deposition in HepG2 cells was significantly better than that of MDL. Molecular networking-based metabolic profiles identified 68 and 67 components in CHL and MDL, with 33 shared components. Extreme gradient boosting (XGBoost) algorithm with outstanding performance was selected to screen candidate components contributing to hypolipidemic activity, and the output was later interpreted by Shapley additive explanations (SHAP) method. Twelve and eight components were separately screened as hyperlipidemic inhibitors in CHL and MDL, while only four constituents were shared. The bioactivity evaluation of selected ingredients and combinations further confirmed their anti-hyperlipidemia capacity. These findings emphasized the feasibility of filtering bioactivity-related compounds using interpretable machine learning approaches and illustrated that related species may contain different hypolipidemic contributors, even if shared constituents existed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the novel Multi-Scale Attention Network (MSA-Net) to address the challenge of locating diverse types and sizes of splicing forgery objects. Previous methods neglect crucial characteristics of global dependencies and size, resulting in imprecise localization on background tampering and small target tampering. To overcome this, we integrate a multi-scale self-attention mechanism to capture global dependencies and fully understand the relationships between spliced objects and untampered areas. Our approach involves inserting multi-scale attention modules that combine the position attention and channel attention modules between convolution layers for feature extraction. The position attention module emphasizes spatial interdependencies, capturing relationships between feature positions. Similarly, the channel attention module captures relationships between channel features. This allows for the preservation of intrinsic details while capturing long-range semantic dependencies, which is beneficial to the detection of splicing forgery objects. Meanwhile, by dividing the feature maps into multiple sub-regions or sub-channels, our attention modules can better preserve the details while capturing long-range semantic information dependencies. Experimental results show that the proposed MSA-Net outperforms several state-of-the-art algorithms with an F1-score of 60.5% and an IOU value of 58.8% on the CASIA dataset. [ABSTRACT FROM AUTHOR]

Due to dynamic changes, applications designed for the Internet of Things (IoT) require new storage resources. Storing IoT data in a fog infrastructure is a common practice to guarantee the IoT application's high performance. Multiple fog nodes spread the data replica to accommodate multiple users and reduce access latency. However, such data replicas are subjected to intentional and accidental corruption. This paper proposes a data integrity verification approach in a distributed fog environment. The proposed work uses fuzzy clustering and consensus mechanism to verify the integrity of multiple replicas stored on distributed fog nodes. Security analysis shows that the proposed approach is highly resistant to replace, replay, and forgery attacks. Performance analysis demonstrates the practicability of the proposed approach in a fog environment. [ABSTRACT FROM AUTHOR]

Quantum secret sharing plays a crucial role in quantum cryptography. In this paper, we present a rational hierarchical (t,n)-threshold quantum secret sharing scheme based on Lagrange interpolation. In our scheme, participants possess rational and hierarchical properties, and the secret can be reconstructed when the number of rational participants satisfies the hierarchical (t,n)-threshold structure proposed in this paper. The reconstructed secret can encompass both classical information and quantum state information, enhancing the practicality and flexibility of our scheme compared to existing ones. Additionally, we redefine the utility of participants based on their roles in the secret recovery process. This newly defined utility allows for a more precise analysis of the correctness, fairness, and equilibrium of our scheme. Finally, our scheme not only resists a typical set of external attacks but also incorporates mechanisms to detect forgery and collusion among participants. [ABSTRACT FROM AUTHOR]

With the development of generative models, abused Deepfakes have aroused public concerns. As a defense mechanism, face forgery detection methods have been intensively studied. Remote photoplethysmography (rPPG) technology extract heartbeat signal from recorded videos by examining the subtle changes in skin color caused by cardiac activity. Since the face forgery process inevitably disrupts the periodic changes in facial color, rPPG signal proves to be a powerful biological indicator for Deepfake detection. Motivated by the key observation that rPPG signals produce unique rhythmic patterns in terms of different manipulation methods, we regard Deepfake detection also as a source detection task. The Multi-scale Spatial–Temporal PPG map is adopted to further exploit heartbeat signal from multiple facial regions. Moreover, to capture both spatial and temporal inconsistencies, we propose a two-stage network consisting of a Mask-Guided Local Attention module (MLA) to capture unique local patterns of PPG maps, and a Temporal Transformer to interact features of adjacent PPG maps in long distance. Abundant experiments on FaceForensics + + and Celeb-DF datasets prove the superiority of our method over all other rPPG-based approaches. Visualization also demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, face forgery detection methods have also achieved remarkable progress. However, most methods suffer significant performance degradation on low-quality compressed face images. It is due to: (a) The image artifacts will be blurred in the process of image compression, resulting in the model learning insufficient artifact traces; (b) Low-quality images will introduce a lot of noise information, and minimizing the training error causes the model to absorb all correlations in the training dataset recklessly, leading to the over-fitting problem. To solve the above problems, we consider learning domain invariant representations to inscribe the correct relevance, i.e., artifacts, to improve the robustness of low-quality images. Specifically, we propose a novel face forgery detector, called DIFLD. The model has the following components: (1) a high-frequency invariant feature learning module(hf-IFLM), which effectively retrieves the blurred artifacts in low-quality compressed images; and (2) a high-dimensional feature distribution learning module(hd-FDLM), that guides the network to learn more about the consistent features of distribution. With the above two modules, the whole framework can learn more discriminative correct artifact features in an end-to-end manner. Through extensive experiments, we show that our proposed method is more robust to image quality variations, especially in low-quality images. Our proposed method achieves a 3.67% improvement over the state-of-the-art methods on the challenging dataset NeuralTextures. [ABSTRACT FROM AUTHOR]

In the real-world scenario, the digital videos are captured to keep in records the life related memories, security purpose, truthful evidence etc. Due to rise in multimedia technology, the digital videos are mostly posted over social websites and applications such as Facebook, Instagram, What's App, YouTube, etc. through the internet in daily routine. The contents of these social digital videos are manipulated by easily available editing tools and software. Therefore, the security of digital videos over social media are the most important requirement of people. This paper presents a systematic survey on Copy-Move forgery and its detection with several passive techniques in the digital videos. At first, Copy-Move forgery is introduced with its various types in this paper. Then, a survey is provided on the existing passive techniques for detecting Copy-Move forgery in digital videos. Here, brief review of techniques is also presented in the tabular forms with their used features, datasets, parameters, type of forgery and their limitations. Furthermore, this survey provides the parameters and datasets in detail which are used for the evaluation of existing techniques. Besides, the detecting tools for Copy-Move video forgery and its future directions are detailed in this survey. This paper also provides various new challenges for automatic detection of Copy-Move video forgery in the realm of deep learning. [ABSTRACT FROM AUTHOR]

The rapid advancement of science and technology has led to the potential for easy manipulation of multimedia content through the use of diverse editing tools. This poses a significant threat to the credibility and integrity of multimedia information. Consequently, substantiating digital images is becoming gradually crucial as digital images hold vital information and are used as essential pieces of evidence in various sectors. The necessity and relevance of digital image forensics have drawn several academics to develop various detection procedures in image forensics. Passive image forgery detection is the foundation of image forensics. Some common passive forgeries that influence the image's authenticity are image splicing, copy-move, and retouching. In recent times, substantial research effort has been devoted to developing novel approaches for detecting several image forgeries. This study provides an overview of similar research efforts that have been carried out utilizing a well-defined methodology. Our goal is to create an efficient way for image forensics researchers to discover new features of forgeries. This study presents a brief introduction to image forensics, including a historical perspective, taxonomy, and framework of image forgery detection approaches. Various resources useful to academic researchers, such as journals, datasets, websites, and performance parameters are explored and presented. This paper will provide a comprehensive review that will aid researchers in overcoming the numerous challenges experienced in earlier studies. Also, future directions are provided to help scholars in this domain. The purpose of this research is to evaluate passive image forgery detection approaches, therefore benefiting new researchers. [ABSTRACT FROM AUTHOR]

Forged images in JPEG format have a ubiquitous presence in today's world due to the ease of availability of image manipulation tools. This paper presents a deep learning-based system that utilizes the inherent relationship between histograms of quantized DCT coefficients and corresponding quantization step sizes to distinguish between original and forged regions in a JPEG image, based on detecting single and double compressed blocks, without fully decompressing the JPEG image. In this direction, we propose a novel combination of raw histograms of the quantized DCT coefficients and corresponding quantization step sizes and use it as input to the counterpart standard CNN architectures designed to handle the proposed input representation for single vs. double JPEG compression detection. The proposed input is shown to have a minimal effect on performance with various standard CNN architectures that are designed to handle the proposed input for the task at hand. Specifically, we have utilized DenseNet to extract the compression-specific artifacts from the proposed input for additional experiments reported in this work. We considered a publicly available dataset generated with a diverse set of 1,120 quantization matrices. Using the proposed input to learn the compression artifacts outperforms the baseline methods for the blocks of sizes 256 × 256 , 128 × 128 , and 64 × 64 . Furthermore, in the case of test blocks compressed with completely different quantization matrices than matrices used in training, the proposed method outperforms the baseline methods. Consequently, improved forgery localization performances are obtained for forged JPEG images. [ABSTRACT FROM AUTHOR]

Image forensics is essential for detecting image manipulation, authenticating images, and identifying sources of images. A forensic analyst can make use of various artifacts to develop a powerful forensic technique. These artifacts include JPEG blocking and quantization artifacts, streaking artifacts and contrast enhancement artifacts, etc. With the introduction of anti-forensics, it has become difficult for forensic experts to identify forged images. There are various anti-forensic methods available that try to eradicate these detection footprints/artifacts to fool the existing forensic detectors. Thus the detection of anti-forensic attacks is very crucial and plays a vital role in forensic analysis. This paper presents a review of various types of anti-forensic attacks, such as JPEG anti-forensics, Contrast enhancement anti-forensics, and Median filtering anti-forensics. Firstly a brief introduction is given about image forgery, JPEG compression, contrast enhancement, and median filtering. Then, anti-forensics is described in detail, and finally, the recent state-of-the-art anti-forensic techniques are summarized in tabular form for better understanding. This may be helpful for the forensic analyst to develop robust methods for forgery detection that can be applied in various applications such as the identification of cybercrimes, identity thefts, etc. [ABSTRACT FROM AUTHOR]

The rapid proliferation of high-quality false images on social media sites calls for research on legitimate image recognition systems. Copy-move forgery (CMF), which involves copying portions of an image, is one of the most commonly used image altering methods. Due to the problem of exploding and vanishing gradients, the present Convolutional Neural Network (CNN) model must be trained for up to 100 epochs to achieve the greatest accuracy. In this work, a deep CNN (DCNN) model using the residual network with 101 deep layers has been used. In order to solve the problem of exploding and disappearing gradients, the concept of skip connections has been included in the residual network. In addition, in order to maximize the performance of the suggested ResNet-101 model, the cyclical learning rate (CLR) hyper-parameter is utilized to further tune the model. The model was trained and evaluated using a variety of datasets, including MICC-F600, MICC-F2000, MICC-F220, and CoMoFoD v2. Accuracy, error rate, true positive rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate (FNR) were analyzed quantitatively. The proposed model achieves highest accuracy of 97.75% only after training the model for 5 epochs only for CoMoFoD v2 dataset. For MICC-F220, MICC-F600 and MICC-F2000 datasets the achieved accuracy was 96.09%, 97.63% and 96.87% respectively only after training the model up to 10 epochs. In order to demonstrate the efficacy of the suggested approach, a comparative study with various state-of-the-art-models available in the literature has been presented. [ABSTRACT FROM AUTHOR]

Malicious individuals can modify speech recordings with advanced audio editing software to create forged audio. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The ordering points to identify the clustering structure method (OPTICS) is used by the approach to match the corresponding descriptors. The proposed approach to eliminate false matches evaluates the correctness of the matches. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature. [ABSTRACT FROM AUTHOR]

Due to their robustness against large-scale geometric transformations, keypoint-based detection methods play an important role in revealing copy–move evidence. However, where copy–move forgeries are only involved in low-contrast or small or smooth regions, these approaches do not yield high-performance results because the number of keypoints in these regions is very limited. We recommend a highly efficient copy–move forgery detection algorithm by ADaptive Scale-Invariant Feature Transform (ADSIFT). Initially, by adapting the gamma factor for contrast threshold and rescaling factor values for feature matching and forgery detection, we produce an adequate number of keypoints that occur even in low-contrast or small or smooth regions of noisy or noiseless images. The proposed approach delivers greater performance than the existing methods as compared to precision and efficiency. [ABSTRACT FROM AUTHOR]

Copy-move forgery detection poses a significant challenge because the brightness and contrast of forged and real regions are highly consistent, and the forensic clues are weakened by various affine transformations and post-processing operations. Existing deep learning models ignore the hierarchical dependencies of the features and pay less attention to information purification. To solve these problems, we propose a novel two-stage model to detect copy-move forgery, dubbed by CCAIR-Net. First, in the coarse localization stage, we propose a cross-scale condition aggregation module to integrate multi-level features from global to local. This aggregation structure can eliminate semantic disparities and capture multi-scale hierarchical dependencies of features. In particular, the condition aggregation block enables feature purification and filtering of interfering information. Second, in the refinement stage, the designed weighted fusion mechanism can guide the model to remove falsely detected regions and supplement the miss-detected regions by adaptively weighted fusion of coarse-grained and fine-grained features. Furthermore, the stage-wise training strategy takes advantage of different losses to train the network to detect tampered regions at various scales. Extensive experiments demonstrate that the proposed CCAIR-Net performs better than state-of-the-art methods. It can detect and segment forged and real areas more accurately, even for affine transformations and post-processing attack images. [ABSTRACT FROM AUTHOR]

With advent of digital devices, we are surrounded by many digital images. We usually believe on digital images in whatever form presented to us. Therefore, we need to be careful as the images may be forged. There exist several image forgeries through which original intent of the image may be hidden and some other meaning is reflected through forgery. Copy-move forgery is one such forgery technique, where the manipulator copies certain portion of the image and duplicates it in some other portion of the same image. In this paper, we propose a novel approach to detect the copy-move forgery in images using non-overlapping block level pixel comparisons and that can achieve better detection and classification accuracy. This approach divides image into 4, 5, 6 or more such blocks and compare each block by moving sliding window over the entire image which is not overlapping with current block. It was found that with different number of blocks the forged region of different sizes can be easily found. We have used SSIM (structure similarity index) parameter to classify the image as forged or original. Algorithm is simulated on various datasets including (MICC, CASIA, coverage, and COMOFOD, etc.) and achieved maximum accuracy of 98% and also compared our result on precision, recall, FPR and FNR including other parameters. [ABSTRACT FROM AUTHOR]

Fingerprints have been widely used and accepted as an effective method of human identification. This biometric tool aids in criminal investigations for personal identity for over a century. Whilst the Automatic Fingerprint Identification System (AFIS) has bolstered security efforts, it has also opened doors to potential scams, affecting both civilian and law enforcement operations. Despite extensive research on fingerprint authentication issues, very little attention has been given to addressing the problem of fingerprint alteration or obfuscation. Fraudsters, with the guidance of experts, have developed new techniques to obscure their fingerprints intentionally. Fingerprint obfuscation is the deliberate alteration of fingerprint patterns with the aim of concealing their true identity, raising concerns amongst security and investigative organizations. The objective of the current communication is to highlight the numerous techniques used for obfuscation, forgery and alteration of fingerprints in humans. It further accentuates the need for identification and interpretation of these altered fingerprints and recommends notifying law enforcement agencies of potential threats. [ABSTRACT FROM AUTHOR]

With the gradual maturity of Smart Grid (SG), security challenges become the most important issues that need to be addressed urgently. In recent years, many schemes adopt mutual authentication and key agreement to ensure secure communication in SG. However, most existing methods have their own shortcomings in either security or efficiency which make them difficult to satisfy the security requirements of SG. In this paper, we propose a provable secure and lightweight authenticated key agreement scheme based on the Elliptic Curve Cryptosystem (ECC) for the cloud-edge-end collaboration SG communication network. The proposed scheme can guarantee the security of the communication and provide anonymity for both the edge server and the smart meters. The anonymity of the smart meters can be provided by a pseudonym mechanism. By rigorous security analysis, the proposed scheme can resist to the typical attacks including replay attacks and identity forgery attacks. The security properties are also evaluated by using the Canetti and Krawczyk (CK) adversary model. The performance simulation results denotes that the proposed scheme not only owns stronger security functions but also improves computation efficiency by 24.5% on average than other four schemes. By simulation results, the proposed scheme has been shown to hold high efficiency. [ABSTRACT FROM AUTHOR]

Deep learning advancements have resulted in breakthroughs in facial forgery techniques. Facial forgery videos are growing increasingly lifelike, making it impossible for people to tell the difference between the real and the fake. The proliferation of facial forgery techniques, as well as the slowness with which they may be detected, could jeopardize personal data security. As a result, it's critical to look at approaches that can be trained on both real and fake videos and then utilized to identify facial forgeries or not in videos. This study found that forged face videos undergo truncation between consecutive frames of optical flow imaging after dense optical flow processing. We present an approach for detecting video face forgery that extracts and analyzes characteristics from real and fake material, then use those features to train classification models on Celeb-DF and FaceForensics++. In addition, we employ a unique facial double-triangle region to assist in the extraction of video inter-frame feature data. Experiments results show that the facial motion features extracted from the double triangle region successfully assist in capturing the dense optical flow truncation. Extensive evaluation suggests that our proposed approach is effective for video face forgery detection. [ABSTRACT FROM AUTHOR]

Land registration system is a very critical component of a revenue department as it holds the record of lands and their owners. In many countries, land records are stored in paper form at centralized locations, making them vulnerable to forgery. Moreover, the centralized system has performance bottleneck, non-transparency, double-spending problems, and a single point of failure. Such issues lead to corruption and conflicts. Recently, Blockchain has been conceived as a technology having the potential to implement and enforce critical security requirements in land revenue systems. With Blockchain, the common issues in revenue systems, such as forgery, falsification, misrepresentation, and human-induced anomalies in records at various levels/stages, can be prevented. The focus of this research is to identify the relationship between Blockchain technology and the land administration system. We discuss issues with current paper-based centralized systems. The potential benefits of Blockchain technology in land management systems are also examined. We present the comparisons of state-of-the-art solutions along with their problems in detail. Finally, research challenges and future directions in Blockchain-enabled land management systems are explored. [ABSTRACT FROM AUTHOR]

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the literature are mostly based on border seizures, but in this paper, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types to those found in seizures but that the share of watches is higher while the share of electronics, clothes, shoes, and Tobacco is lower on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the equivalent products on the surface web appear to be advertised for a multiple of the prices found for darknet markets. We provide some suggestions on how information about darknet market counterfeits could be used by companies and authorities for preventative purposes, showing that insight gathering from the dark web is valuable and could be a cost-effective alternative (or compliment) to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better. [ABSTRACT FROM AUTHOR]

This paper uses learning-based color illumination techniques for the classification and localization of passive image geometric attack forgery classification and localization. Copy move and splicing forgery are classified using CNN. The classification accuracy obtained during validation for CASIAv1.0 is 97.35, CASIAv2.0 is 97.93, and DVMM is 97.86. The large data set is created to classify geometrical attacks by combining all the data sets with rotation and scale artifacts. The classification accuracy between rotation and scale during validation is 99.29. Machine learning-based color illumination technique is used for localization of forgery. An experiment was conducted on the CoMoFoD data set to detect passive image and geometric attack forgery. There are 48 images in the dataset with various geometric attacks such as scale and rotation. The results for identifying simple CMF attacks show an F1 score of 98.53%, a precision rate of 97.25%, and a recall rate of 100%. In the case of detecting CMF attacks on a larger scale, the F1 score is 79.1%, the precision rate is 85.2%, and the recall rate is 74.8%. For CMF attack rotation, the F1 score is 86.16%, the precision rate is 87.83%, and the recall rate is 76.33%. The proposed method demonstrates improved accuracy in detecting forgeries compared to existing approaches. [ABSTRACT FROM AUTHOR]

As manipulated faces become more realistic and indistinguishable, there is a high demand for efficiently and accurately detecting deepfakes. Existing CNN-based deepfake detection methods either learn a global feature representation of the whole face or learn multiple local features. However, these methods learn the global and local features independently, thus neglect the spatial correlations between the local features and global context, which are vital in identifying different forgery patterns. Therefore, in this paper, we propose Spatial Interaction Network (SI-Net), a deepfake detection method to mine potential complementary and co-occurrent features between local texture and global context concurrently. Specifically, we first utilize a region feature extractor that distills local features from the global features, to simplify the procedure of local feature extraction. We then propose spatial-aware transformer to learn the co-occurrence feature from local texture and global context, concurrently. We capture the attended feature from the local regions according to their importance. The final prediction is made through the composite considerations of the aforementioned modules. Experimental results on two public datasets, FaceForensics++ and WildDeepfake, demonstrate the superior performance of SI-Net compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video plays a key role in carrying authenticity, especially in the surveillance system, medical field, court evidence, journalism, and social media among others. However, nowadays the trust in videos is decreasing day by day due to the forgery of the videos made by easily accessible video editing tools. Hence, a thrust for finding a robust solution to the problem of video forgery detection arises. As a result, researchers around the world are indulging themselves to come up with various methods for the said problem. In this article, we have comprehensively discussed many such initiatives made by researchers across the globe, keeping the focus on recent trends. In addition to this, we have also covered a wide range of forgery detection techniques that follow either an active or a passive approach, while the state-of-the-art surveys made so far on this research topic include only a few specific cases. In this article, we have described some recent technologies that are used in video forging, made a summary of the performances (provided categorically) of all the techniques discussed here, and briefed the available datasets. Finally, we have concluded this survey by clearly mentioning some future directions of the video forgery detection research based on a thorough review of existing techniques. [ABSTRACT FROM AUTHOR]