arXiv AI By Yuyang Zhang, Kangjie Chen, Yihui Jin, Run Wang

Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices

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arXiv Computer Vision
Sep 21

Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

The paper "Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses" provides a comprehensive review of model inversion (MI) attacks that exploit trained deep neural networks to reconstruct private training data. It traces the evolution of MI from early machine‑learning contexts to recent DNN‑based attacks across various modalities and learning tasks, offering a detailed taxonomy and comparative analysis of both attacks and defenses. The authors also present an open‑source toolbox on GitHub to support further research in this area.

By Hao Fang, Yixiang Qiu, Hongyao Yu, Wenbo Yu, Jiawei Kong, Baoli Chong, Bin Chen, Xuan Wang, Shu-Tao Xia, Ke Xu
arXiv AI
Oct 19

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

The paper surveys transferable attacks on AI systems, noting that such attacks can be crafted in one setting and then applied to others, thereby lowering costs and increasing security risks. It introduces a unified taxonomy across six axes—cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-deployment—while distinguishing source-target changes from attacker resources. The survey also links transfer mechanisms to enhancement methods, highlights inconsistencies and gaps in current research, and proposes a research agenda for better benchmarking and defense strategies.

By Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan