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Automated content filtering and moderation is an important tool that allows online platforms to build striving user communities that facilitate cooperation and prevent abuse.
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R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=Bygh9j09KX
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2021
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2021
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2022
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H. Salman, S. Jain, E. Wong, and A. Madry, “Certified patch robustness via smoothed vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 15 137–15 147
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G. Apruzzese, H. S. Anderson, S. Dambra, D. Freeman, F. Pierazzi, and K. Roundy, ““real attackers don’t compute gradients”: Bridging the gap between adversarial ml research and practice,” in 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) . IEEE, 2023, pp. 339–364
2023
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S.-A. Rebuffi, F. Croce, and S. Gowal, “Revisiting adapters with adversarial training,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=HPdxC1THU8T
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C. Xiang, S. Mahloujifar, and P. Mittal, “ { \{ PatchCleanser } \} : Certifiably robust defense against adversarial patches for any image classifier,” in 31st USENIX Security Symposium (USENIX Security 22) , 2022, pp. 2065–2082
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