Dr. Eric Chan-Tin is a Professor of Computer Science at Loyola University Chicago, where he also serves as Graduate Program Director for Computer Science and Software Engineering. His research focuses on network security, distributed systems, privacy, and anonymity, including Tor, browser fingerprinting, and the human factors of cybersecurity. He is also involved in cybersecurity education and leadership through Loyola’s Center of Cybersecurity and NSA/DHS Center of Academic Excellence in Cyber Defense.
Publications (9)
2025
Stealthy Query-Efficient OpaqueAttack Against Interpretable Deep Learning
IEEE Transactions on Reliability
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01 Sep 2025
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doi:10.1109/TR.2025.3551717
2024
Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses
IEEE TDSC
IEEE Transactions on Dependable and S...
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01 Jul 2024
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doi:10.1109/TDSC.2023.3341090
Unmasking the Vulnerabilities of Deep Learning Models: A Multi-Dimensional Analysis of Adversarial Attacks and Defenses
2024 Silicon Valley Cybersecurity Con...
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17 Jun 2024
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doi:10.1109/SVCC61185.2024.10637364
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
arXiv
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06 May 2024
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arxiv:2405.01963
2023
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks
arXiv
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25 Jul 2023
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arxiv:2307.11906
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems
arXiv
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14 Jul 2023
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arxiv:2307.06496
2022
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning
arXiv
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30 Nov 2022
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arxiv:2211.15926
MLxPack: Investigating the Effects of Packers on ML-based Malware Detection Systems Using Static and Dynamic Traits
Proceedings of the 1st Workshop on Cy...
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30 May 2022
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doi:10.1145/3494108.3522768
2021
AdvEdge: Optimizing Adversarial Perturbations Against Interpretable Deep Learning
Lecture Notes in Computer Science
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01 Jan 2021
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doi:10.1007/978-3-030-91434-9_9