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 1301–1310 of 1808 papers

TitleStatusHype
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence—0
Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Weighted-Sampling Audio Adversarial Example Attack—0
Query-Efficient Black-Box Attack by Active Learning—0
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers—0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—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