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ACSAC'2024
ACSAC'2024
On the Credibility of Backdoor Attacks Against Object Detectors in the Physical World
Bao Gia Doan, Dang Quang Nguyen, Callum Lindquist, Paul Montague, Tamas Abraham, Olivier De Vel, Seyit Camtepe, Salil S Kanhere, Ehsan Abbasnejad, Damith C Ranasinghe
ESORICS'2024
ESORICS'2024
Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague, Tamas Abraham, Olivier De Vel, Seyit Camtepe, Salil S Kanhere, Ehsan Abbasnejad, Damith C Ranasinghe
AAAI'2023
AAAI'2023
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness
Bao Gia Doan, Shuiqiao Yang, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Salil S Kanhere, Ehsan Abbasnejad, Damith C Ranasinghe
ICML'2022
ICML'2022
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan M Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe
IEEE Transactions on Information Forensics and Security (TIFS)
IEEE Transactions on Information Forensics and Security (TIFS)
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith C Ranasinghe
ACSAC'2020
ACSAC'2020
Februus: Input purification defense against trojan attacks on deep neural network systems
Bao Gia Doan, Ehsan Abbasnejad, Damith C Ranasinghe
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