black-box attack

Boosting the Transferability of Adversarial Samples via Attention CVPR 2020 ColorFool: Semantic Adversarial Colorization CVPR 2020 DaST: Data-Free Substitute Training for Adversarial Attacks CVPR 2020 Defending Against Model Stealing Attacks With Adaptive Misinformation CVPR 2020 Defense for Black-Box Attacks on Anti-Spoofing Models by Self-Supervised Learning Interspeech 2020 Diversity can be Transferred: Output Diversification for White- and Black-box Attacks NeurIPS 2020 Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks ICML 2020 Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction CVPR 2020 Enhancing Transferability of Black-Box Adversarial Attacks via Lifelong Learning for Speech Emotion Recognition Models Interspeech 2020 Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples ACL 2020 GeoDA: A Geometric Framework for Black-Box Adversarial Attacks CVPR 2020 Heuristic Black-Box Adversarial Attacks on Video Recognition Models AAAI 2020 Learning Black-Box Attackers with Transferable Priors and Query Feedback NeurIPS 2020 Learning Transferable Adversarial Examples via Ghost Networks AAAI 2020 Leveraging Extracted Model Adversaries for Improved Black Box Attacks EMNLP 2020 On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks CVPR 2020 Polishing Decision-Based Adversarial Noise With a Customized Sampling CVPR 2020 Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks ICLR 2020 Projection & Probability-Driven Black-Box Attack CVPR 2020 QEBA: Query-Efficient Boundary-Based Blackbox Attack CVPR 2020 Query-efficient Meta Attack to Deep Neural Networks ICLR 2020 Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples EMNLP 2020 Sign Bits Are All You Need for Black-Box Attacks ICLR 2020 Sign-OPT: A Query-Efficient Hard-label Adversarial Attack ICLR 2020 Suspicion-Free Adversarial Attacks on Clustering Algorithms AAAI 2020