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Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks., , , , , , , , and . CoRR, (2023)Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference., , , , , , , and . CoRR, (2021)Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation., , , , and . CoRR, (2022)Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates., , , , , , and . CoRR, (2024)Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions., , , , , , and . CoRR, (2021)Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks., , and . ACM Comput. Surv., 52 (4): 78:1-78:36 (2019)Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning., , , , , , , , , and . ACM Comput. Surv., 55 (13s): 294:1-294:39 (2023)Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware., and . IEEE Secur. Priv., 17 (1): 63-71 (2019)Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers., , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (12): 9944-9959 (2022)Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware., , , , and . IEEE Trans. Inf. Forensics Secur., (2021)