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Titlebook: Adversarial Multimedia Forensics; Ehsan Nowroozi,Kassem Kallas,Alireza Jolfaei Book 2024 The Editor(s) (if applicable) and The Author(s),

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發(fā)表于 2025-3-23 10:39:47 | 只看該作者
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E. Casas,J.-P. Raymond,H. Zidaniwe analyze the performance of Comprint when manipulated images are exposed to differences in JPEG compression, such as small quality factor differences, non-standard quantization tables, and DCT implementations, as well as when exposed to recompression. The results demonstrate that Comprint is robus
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https://doi.org/10.1007/978-3-540-71119-3es of attacks. Randomization of features was found to contribute to disrupting attack transferability, even in cases where attack transferability can be prevented by retraining the detector or simply varying the detector architecture.
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發(fā)表于 2025-3-25 00:56:54 | 只看該作者
https://doi.org/10.1007/978-3-540-71119-3with the vocoder identification system. We employ a self-supervised representation learning (SSRL) approach, treating vocoder identification as a pretext task. Doing so ensures that the front-end feature extraction module is constrained and optimized to build the final binary classifier for syntheti
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