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Mitigating Adversarial Effects Through Randomization.

, , , , и . ICLR (Poster), OpenReview.net, (2018)

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Jian Xie

Yangpei Xie

Zhenwei Xie

Mingliang Xie

Lan Xie

 

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On the Adversarial Robustness of Camera-based 3D Object Detection., , , и . CoRR, (2023)Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses., , , , , и . CoRR, (2019)Simulated Adversarial Testing of Face Recognition Models., , , , , , и . CoRR, (2021)Practical Disruption of Image Translation Deepfake Networks., , , и . AAAI, стр. 14478-14486. AAAI Press, (2023)Adversarial Examples Improve Image Recognition., , , , , и . CVPR, стр. 816-825. Computer Vision Foundation / IEEE, (2020)Robust and Accurate Object Detection via Adversarial Learning., , , , , и . CVPR, стр. 16622-16631. Computer Vision Foundation / IEEE, (2021)Simulated Adversarial Testing of Face Recognition Models., , , , , , и . CVPR, стр. 4135-4145. IEEE, (2022)PatchAttack: A Black-Box Texture-Based Attack with Reinforcement Learning., , , , и . ECCV (26), том 12371 из Lecture Notes in Computer Science, стр. 681-698. Springer, (2020)One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks., , , и . ICLR, OpenReview.net, (2023)FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning., , , , , , и . Trans. Mach. Learn. Res., (2024)