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Model fragile watermarking, inspired by both the field of adversarial attacks on neural networks and traditional multimedia fragile watermarking, has gradually emerged as a potent tool for detecting tampering, and has witnessed rapid development in r
Externí odkaz:
http://arxiv.org/abs/2406.04809
Publikováno v:
IEEE International Conference on Multimedia and Expo 2024
Neural networks have increasingly influenced people's lives. Ensuring the faithful deployment of neural networks as designed by their model owners is crucial, as they may be susceptible to various malicious or unintentional modifications, such as bac
Externí odkaz:
http://arxiv.org/abs/2404.07572
Publikováno v:
The paper is under consideration at Pattern Recognition Letters, Elsevier, 2023
Artificial Intelligence (AI) has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique
Externí odkaz:
http://arxiv.org/abs/2308.11235
Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them after uploading to the cloud or transferring from a local system. To addre
Externí odkaz:
http://arxiv.org/abs/2305.09684
Autor:
Cao, Yuefen, Zhao, Xuan, Tang, Mengling, Sun, Chendong, Ding, Mingquan, Song, Yuchen, Lan, Jiakai, Gao, Zhenzhe, Rong, Junkang
Publikováno v:
In Industrial Crops & Products 1 October 2024 217
Publikováno v:
In Pattern Recognition Letters April 2024 180:9-15
Publikováno v:
In Journal of Information and Intelligence January 2024 2(1):28-41
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