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Improving Adversarial Transferability via Frequency-based Stationary Point Search., , , , , , , and . CIKM, page 3626-3635. ACM, (2023)GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model., , , , , , , and . SDM, page 706-714. SIAM, (2024)MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks., , , , , , , and . AAAI, page 17228-17236. AAAI Press, (2024)MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks., , , , , , , and . CoRR, (2023)POSTER: ML-Compass: A Comprehensive Assessment Framework for Machine Learning Models., , , , and . AsiaCCS, page 1031-1033. ACM, (2023)DANAA: Towards Transferable Attacks with Double Adversarial Neuron Attribution., , , , , and . ADMA (2), volume 14177 of Lecture Notes in Computer Science, page 456-470. Springer, (2023)DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks., , , , and . CoRR, (2024)FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning., , , , , , , , and . CIKM, page 3657-3666. ACM, (2023)Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning., , , , , , , and . ECML/PKDD (1), volume 14169 of Lecture Notes in Computer Science, page 147-163. Springer, (2023)