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 1451–1460 of 1808 papers

TitleStatusHype
Minority Reports Defense: Defending Against Adversarial Patches—0
Transferable Perturbations of Deep Feature Distributions—0
Towards Feature Space Adversarial AttackCode1
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model—0
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack—0
Improved Adversarial Training via Learned Optimizer—0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers—0
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
BERT-ATTACK: Adversarial Attack Against BERT Using BERTCode1
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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