Search Results for author: Bao Gia Doan

Found 6 papers, 4 papers with code

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

1 code implementation5 Dec 2022 Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe

We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal.

Adversarial Defense

TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems

no code implementations19 Nov 2021 Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith C. Ranasinghe

Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective achieving high attack success rates, and universal.

Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

1 code implementation21 Jul 2020 Yansong Gao, Bao Gia Doan, Zhi Zhang, Siqi Ma, Jiliang Zhang, Anmin Fu, Surya Nepal, Hyoungshick Kim

We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor. Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks.

Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems

1 code implementation9 Aug 2019 Bao Gia Doan, Ehsan Abbasnejad, Damith C. Ranasinghe

Notably, in contrast to existing approaches, our approach removes the need for ground-truth labelled data or anomaly detection methods for Trojan detection or retraining a model or prior knowledge of an attack.

Cryptography and Security

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