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 1261–1270 of 1808 papers

TitleStatusHype
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—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