neural network robustness

Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption AAAI 2021 Feature Space Targeted Attacks by Statistic Alignment IJCAI 2021 Generating adversarial examples with graph neural networks UAI 2021 Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering NeurIPS 2021 Improving the Transferability of Adversarial Samples With Adversarial Transformations CVPR 2021 Morié Attack (MA): A New Potential Risk of Screen Photos NeurIPS 2021 Probabilistic Robustness Quantification of Neural Networks AAAI 2021 Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack CVPR 2021 Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective AAAI 2021 Adversarial Examples for Edge Detection: They Exist, and they Transfer WACV 2020 Exactly Computing the Local Lipschitz Constant of ReLU Networks NeurIPS 2020 Jacobian Adversarially Regularized Networks for Robustness ICLR 2020 Modeling Biological Immunity to Adversarial Examples CVPR 2020 One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples CVPR 2020 Robust Adversarial Objects against Deep Learning Models AAAI 2020 Robust Design of Deep Neural Networks Against Adversarial Attacks Based on Lyapunov Theory CVPR 2020 SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations WACV 2020 Towards Certificated Model Robustness Against Weight Perturbations AAAI 2020 Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations CVPR 2020 Accurate, reliable and fast robustness evaluation NeurIPS 2019 Adversarial Attacks Beyond the Image Space CVPR 2019 Approximate Feature Collisions in Neural Nets NeurIPS 2019 A Statistical Approach to Assessing Neural Network Robustness ICLR 2019 Attacking Optical Flow ICCV 2019 AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks AAAI 2019