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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 431–440 of 1808 papers

TitleStatusHype
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Augmented Adversarial Trigger Learning—0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—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