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Black-box and Target-specific Attack Against Interpretable Deep Learning Systems.

, , , and . AsiaCCS, page 1216-1218. ACM, (2022)

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Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems., , , , and . CoRR, (2023)AdvEdge: Optimizing Adversarial Perturbations Against Interpretable Deep Learning., , , , and . CSoNet, volume 13116 of Lecture Notes in Computer Science, page 93-105. Springer, (2021)Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models., , , and . AsiaCCS, page 1207-1209. ACM, (2022)Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning., , , , and . CoRR, (2022)MLxPack: Investigating the Effects of Packers on ML-based Malware Detection Systems Using Static and Dynamic Traits., , , , and . CySSS@AsiaCCS, page 11-18. ACM, (2022)Single-Class Target-Specific Attack against Interpretable Deep Learning Systems., , , , and . CoRR, (2023)Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks., , , , and . CoRR, (2023)Black-box and Target-specific Attack Against Interpretable Deep Learning Systems., , , and . AsiaCCS, page 1216-1218. ACM, (2022)An Exploration of Geo-temporal Characteristics of Users' Reactions on Social Media During the Pandemic., , , and . CoRR, (2021)Leveraging Spectral Representations of Control Flow Graphs for Efficient Analysis of Windows Malware., , , and . AsiaCCS, page 1240-1242. ACM, (2022)