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AISec '23: 16th ACM Workshop on Artificial Intelligence and Security., , and . CCS, page 3666-3668. ACM, (2023)Explainability-based Debugging of Machine Learning for Vulnerability Discovery., , and . ARES, page 113:1-113:8. ACM, (2022)Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints., , , and . NeurIPS, page 20052-20062. (2021)AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples., , , , , , , and . CoRR, (2024)Explaining Machine Learning DGA Detectors from DNS Traffic Data., , , and . ITASEC, volume 3260 of CEUR Workshop Proceedings, page 150-168. CEUR-WS.org, (2022)Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors., , , , , and . AISec@CCS, page 233-244. ACM, (2023)AI Security and Safety: The PRALab Research Experience., , , , , , , , and . Ital-IA, volume 3486 of CEUR Workshop Proceedings, page 324-328. CEUR-WS.org, (2023)Robust Machine Learning for Malware Detection over Time., , , and . ITASEC, volume 3260 of CEUR Workshop Proceedings, page 169-180. CEUR-WS.org, (2022)Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving., , , , , , and . ICMLC, page 57-62. IEEE, (2023)Optimization and deployment of CNNs at the edge: the ALOHA experience., , , , , , , , , and 8 other author(s). CF, page 326-332. ACM, (2019)