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Creators/Authors contains: "Zhu, Liuwan"

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  1. ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    This paper proposes SEER, a novel backdoor detection algorithm for vision-language models, addressing the gap in the literature on multi-modal backdoor detection. While backdoor detection in single-modal models has been well studied, the investigation of such defenses in multi-modal models remains limited. Existing backdoor defense mechanisms cannot be directly applied to multi-modal settings due to their increased complexity and search space explosion. In this paper, we propose to detect backdoors in vision-language models by jointly searching image triggers and malicious target texts in feature space shared by vision and language modalities. Our extensive experiments demonstrate that SEER can achieve over 92% detection rate on backdoor detection in vision-language models in various settings without accessing training data or knowledge of downstream tasks. 
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  2. ; ; ; ; (, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))
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  3. ; ; ; ; (, MM '20: Proceedings of the 28th ACM International Conference on Multimedia)
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  4. ; ; ; ; ; ; ; (, The Journal of Computational Science Education)
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