- Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks?
- Ensemble Adversarial Training: Attacks and Defenses
- Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
- Towards Deep Learning Models Resistant to Adversarial Attacks
- Countering Adversarial Images using Input Transformations
- Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
- Mitigating Adversarial Effects Through Randomization
- Adversarial Logit Pairing
- Automated Verification of Neural Networks: Advances, Challenges and Perspectives
- Adversarial examples from computational constraints
- PAC-learning in the presence of evasion adversaries
- On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
- ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
- Feature Denoising for Improving Adversarial Robustness
- Theoretically Principled Trade-off between Robustness and Accuracy
- Improving Adversarial Robustness via Promoting Ensemble Diversity
- Adversarial Examples Are a Natural Consequence of Test Error in Noise
- On Evaluating Adversarial Robustness
- Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
- Adversarial Training for Free!
- You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
- Adversarial Examples Are Not Bugs, They Are Features
- Interpreting Adversarially Trained Convolutional Neural Networks
- Are Labels Required for Improving Adversarial Robustness?
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
- Adversarial Robustness through Local Linearization
- Natural Adversarial Examples
- Adversarial Examples Improve Image Recognition
- Robustness of classifiers: from adversarial to random noise
- Deep Defense: Training DNNs with Improved Adversarial Robustness
- Adversarial vulnerability for any classifier
- Towards Robust Detection of Adversarial Examples
- Adversarially Robust Generalization Requires More Data
- A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
- Scaling provable adversarial defenses
- A Spectral View of Adversarially Robust Features
- Robust Decision Trees Against Adversarial Examples
- Max-Mahalanobis Linear Discriminant Analysis Networks
- Barrage of Random Transforms for Adversarially Robust Defense
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