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Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy.

, , , and . Preregister@NeurIPS, volume 148 of Proceedings of Machine Learning Research, page 325-342. PMLR, (2020)

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UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging., , , , and . NeurIPS, (2020)Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy., , , and . Preregister@NeurIPS, volume 148 of Proceedings of Machine Learning Research, page 325-342. PMLR, (2020)A Survey on Universal Adversarial Attack., , , , , and . IJCAI, page 4687-4694. ijcai.org, (2021)Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind Watermarking., , , and . ACM Multimedia, page 5158-5166. ACM, (2021)Motionsnap: A Motion Sensor-Based Approach for Automatic Capture and Editing of Photos and Videos on Smartphones., , , , and . ICME, page 1-6. IEEE, (2021)Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs., , , , and . BMVC, page 25. BMVA Press, (2021)Simple Techniques are Sufficient for Boosting Adversarial Transferability., , , , and . ACM Multimedia, page 8486-8494. ACM, (2023)Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective., , , and . AAAI, page 3296-3304. AAAI Press, (2021)Data-free Universal Adversarial Perturbation and Black-box Attack., , , and . ICCV, page 7848-7857. IEEE, (2021)Revisiting Batch Normalization for Improving Corruption Robustness., , , and . WACV, page 494-503. IEEE, (2021)