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Poster: Attacking Malware Classifiers by Crafting Gradient-Attacks that Preserve Functionality., , and . ACM Conference on Computer and Communications Security, page 2565-2567. ACM, (2019)Is Data Clustering in Adversarial Settings Secure?, , , , , and . CoRR, (2018)Randomized Prediction Games for Adversarial Machine Learning., , , , and . CoRR, (2016)secml: Secure and explainable machine learning in Python., , , , , and . SoftwareX, (2022)A survey and experimental evaluation of image spam filtering techniques., , , and . Pattern Recognit. Lett., 32 (10): 1436-1446 (2011)FADER: Fast adversarial example rejection., , , , and . Neurocomputing, (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)