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 1061–1070 of 1808 papers

TitleStatusHype
L_p-norm Distortion-Efficient Adversarial Attack—0
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models—0
Make the Most of Everything: Further Considerations on Disrupting Diffusion-based Customization—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
AdvHaze: Adversarial Haze Attack—0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning—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