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Machine learning with adversarial perturbations and noisy labels.. University of Melbourne, Parkville, Victoria, Australia, (2018)base-search.net (ftumelbourne:oai:jupiter.its.unimelb.edu.au:11343/219680).Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better., , , and . ICCV, page 16423-16432. IEEE, (2021)Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks., , , , , and . NeurIPS, page 5545-5559. (2021)Dual Head Adversarial Training., , , and . IJCNN, page 1-8. IEEE, (2021)Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting., , , , , , and . IJCAI, page 786-792. ijcai.org, (2021)Sub-trajectory Similarity Join with Obfuscation., , , , and . SSDBM, page 181-192. ACM, (2021)Short-Term and Long-Term Context Aggregation Network for Video Inpainting., , , , , , , and . ECCV (4), volume 12349 of Lecture Notes in Computer Science, page 728-743. Springer, (2020)A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models., , , , , and . CoRR, (2024)ModelLock: Locking Your Model With a Spell., , , , and . CoRR, (2024)The Dog Walking Theory: Rethinking Convergence in Federated Learning., , , , , and . CoRR, (2024)