Titlebook: Digital Forensics and Watermarking; 22nd International W Bin Ma,Jian Li,Qi Li Conference proceedings 2024 The Editor(s) (if applicable) and

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Pessary

https://doi.org/10.1007/978-3-658-05999-6tion. To address this problem, an image-perceptual hashing algorithm based on convolutional neural networks is proposed in this paper. First of all, the entire image is convolved by the backbone network to obtain a feature map. The Region Proposal Network (RPN) is employed to generate multiple-sized

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https://doi.org/10.1007/978-3-658-26941-8corresponding to three public presentation attack datasets. For the assessment of these synthetic samples, we analyse their behaviour when attacking finger vein recognition systems, comparing these results to such obtained from actually crafted presentation attack samples. We observe that although v

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upstart

https://doi.org/10.1007/978-3-658-26941-8for authentication. A specific hole is added to the non-critical flat portion of the 3D printed object, and the 3DPS is constructed from the contour of the hole with a hand-held microscope. Compared to the previous work, the equipment distortion model and the 3DPS’s construction are improved, and th

DIKE

https://doi.org/10.1007/978-3-658-12786-2visual artifacts. Given the pivotal role facial features play in identity recognition, the misuse of these manipulated images raises significant security concerns, particularly around identity forgery. While existing image forensics algorithms primarily concentrate on traditional tampering methods l

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Autobiography

Datenübersicht (deskriptive Ergebnisse)sed on these traces, several forensic methods have achieved remarkable detection accuracy and strong generalization across different generative models. To counter forensic methods and identify potential vulnerabilities in detectors, existing anti-forensics methods primarily focus on embedding advers

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anniversary