This work proves that the operational validity of the CLWE backdoor critically hinges on assumptions that are incompatible with the realistic RFF learning deployment and analyzes the adversarial robustness of RFF learning models and provides a concrete certified robustness analysis, enabling a deeper security assessmen...
Tianshuo Cong, Pei Li, Hao-Jie Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed. To fill this gap, we present FakeI...
Pei Li, Sihan Chen, Delong Ran et al.· Proceedings of the 32nd ACM...· 0 citations
GhostVAE is proposed to plant a stealthy backdoor into the encoder of Variational Autoencoder (VAE), enabling reliable evasion of watermark detection and fundamentally undermines the trustworthiness of semantic watermarking systems.
Jinyuan Liu, Tianshuo Cong, Pei Li et al.· 0 citations
Random Fourier Features (RFF) learning is a classical technique in scalable data mining. However, at FOCS 2022, Goldwasser et al. proposed a theoretical framework for planting cryptographically undetectable backdoors in RFF learning based on the hardness of the Continuous Learning With Errors (CLWE) problem. Their cons...
Tianshuo Cong, Pei Li, Haojie Wu et al.· Proceedings of the 32nd ACM...· 0 citations
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