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GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems.

, , , , , and . EuroS&P, page 664-683. IEEE, (2022)

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Leveraging Image Processing Techniques to Thwart Adversarial Attacks in Image Classification., , , and . ISM, page 184-191. IEEE, (2019)GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems., , , , , and . EuroS&P, page 664-683. IEEE, (2022)Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks., , , , , and . CCS, page 786-800. ACM, (2023)Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks., , , , , and . CoRR, (2023)Concept-based Explanations for Out-Of-Distribution Detectors., , , , , and . CoRR, (2022)Essential Features: Reducing the Attack Surface of Adversarial Perturbations with Robust Content-Aware Image Preprocessing., , and . CoRR, (2020)Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples., , , , and . CoRR, (2021)Understanding the Impact of Compression on Feature Detection and Matching in Computer Vision., , , and . ISM, page 457-462. IEEE Computer Society, (2016)Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification., , , , , and . CoRR, (2020)Concept-based Explanations for Out-of-Distribution Detectors., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 5817-5837. PMLR, (2023)