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In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity. [ABSTRACT FROM AUTHOR]

The dispute over the authenticity of video has become a hot topic in judicial practice in recent years. Despite detection methods being updated rapidly, methods for determining authenticity have limitations, especially against high-level forgery. Deleting the integral group of pictures (GOP) length in static scenes could remove key information in the video, leading to unjust sentencing. Anyone can conduct such an operation using publicly available software, thus escaping state-of-the-art detection methods. In this paper, we propose a detection method based on noise transfer matrix analysis. A pyramid structure and a weight learning module are adopted to improve the detection rate and reduce the false positive rate. In total, 80 videos were examined through delicate anti-forensic forgery operations to verify the detection performance of the proposed method and three previously reported methods against anti-forensic forgery operations. In addition, two of the latest learning-based methods were included in our experiments to evaluate the proposed method. The experimental results show that the proposed method significantly improves the detection of frame deletion points compared with traditional and learning-based methods, especially in low false positive rate (FPR) intervals, which is meaningful in forensic science. [ABSTRACT FROM AUTHOR]

The current deep learning methods for copy–move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset. [ABSTRACT FROM AUTHOR]

As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender's identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender's identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). [ABSTRACT FROM AUTHOR]