Adversarial Attack Detection
14 papers with code • 0 benchmarks • 0 datasets
The detection of adversarial attacks.
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Latest papers
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
KaosEngineer/PriorNetworks
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NeurIPS 2019
Second, taking advantage of this new training criterion, this paper investigates using Prior Networks to detect adversarial attacks and proposes a generalized form of adversarial training.
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.
