adversarial perturbation

A Geometric Perspective on the Transferability of Adversarial Directions AISTATS 2019 A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations CVPR 2019 A New Defense Against Adversarial Images: Turning a Weakness into a Strength NeurIPS 2019 Boosting Robustness Certification of Neural Networks ICLR 2019 Computational Limitations in Robust Classification and Win-Win Results COLT 2019 Data Fine-Tuning AAAI 2019 Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses CVPR 2019 Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search CVPR 2019 Does Data Augmentation Lead to Positive Margin? ICML 2019 Effective Adversarial Regularization for Neural Machine Translation ACL 2019 Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness NeurIPS 2019 First-Order Adversarial Vulnerability of Neural Networks and Input Dimension ICML 2019 Fisher-Bures Adversary Graph Convolutional Networks UAI 2019 Generating 3D Adversarial Point Clouds CVPR 2019 Hilbert-Based Generative Defense for Adversarial Examples ICCV 2019 Improving Black-box Adversarial Attacks with a Transfer-based Prior NeurIPS 2019 Improving the Generalization of Adversarial Training with Domain Adaptation ICLR 2019 Interpretation of Neural Networks Is Fragile AAAI 2019 Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder NeurIPS 2019 Low Frequency Adversarial Perturbation UAI 2019 Monge blunts Bayes: Hardness Results for Adversarial Training ICML 2019 On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models NAACL 2019 On Relating Explanations and Adversarial Examples NeurIPS 2019 On the Effectiveness of Low Frequency Perturbations IJCAI 2019 On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions CVPR 2019