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 1001–1010 of 1808 papers

TitleStatusHype
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy—0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy—0
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks—0
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters—0
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers—0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses—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