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Found 6 papers, 4 papers with code
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
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.
Transferable Graph Backdoor Attack
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
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
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.
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
In particular, for vision tasks, we can always achieve a 0% FRR and FAR.
Cryptography and Security
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
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
