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 1161–1170 of 1808 papers

TitleStatusHype
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Universal Adversarial Attack Using Very Few Test Examples—0
Universal Adversarial Perturbations and Image Spam Classifiers—0
Universal Attacks on Equivariant Networks—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack 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