SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 1181–1190 of 1808 papers

TitleStatusHype
On the feasibility of attacking Thai LPR systems with adversarial examples—0
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Towards Security Threats of Deep Learning Systems: A Survey—0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network—0
On the reversibility of adversarial attacks—0
On the Robustness of Domain Adaption to Adversarial Attacks—0
Adversarially robust generalization theory via Jacobian regularization for deep neural networks—0
On the Robustness of Split Learning against Adversarial Attacks—0
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified