adversarial transferability

Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement CVPR 2024 Learning to Transform Dynamically for Better Adversarial Transferability CVPR 2024 LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate AAAI 2024 Towards Transferable Adversarial Attacks with Centralized Perturbation AAAI 2024 Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness NeurIPS 2024 An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability ICCV 2023 Backpropagation Path Search On Adversarial Transferability ICCV 2023 Boosting Adversarial Transferability by Achieving Flat Local Maxima NeurIPS 2023 Boosting Adversarial Transferability using Dynamic Cues ICLR 2023 Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently WACV 2023 Improving Adversarial Transferability via Intermediate-level Perturbation Decay NeurIPS 2023 Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability NeurIPS 2023 Rethinking the Backward Propagation for Adversarial Transferability NeurIPS 2023 Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models ICCV 2023 Towards Transferable Targeted Adversarial Examples CVPR 2023 360-Attack: Distortion-Aware Perturbations From Perspective-Views CVPR 2022 Boosting Black-Box Attack With Partially Transferred Conditional Adversarial Distribution CVPR 2022 Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems ICLR 2022 On Improving Adversarial Transferability of Vision Transformers ICLR 2022 Random Normalization Aggregation for Adversarial Defense NeurIPS 2022 Rethinking Adversarial Transferability from a Data Distribution Perspective ICLR 2022 Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability CVPR 2022 A Unified Approach to Interpreting and Boosting Adversarial Transferability ICLR 2021 Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective ICCV 2021 Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference ICML 2021