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 1741–1750 of 1808 papers

TitleStatusHype
A Non-monotonic Smooth Activation Function—0
Effective black box adversarial attack with handcrafted kernels—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
Stabilizing Deep Tomographic Reconstruction—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—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