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 411–420 of 1808 papers

TitleStatusHype
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception—0
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models—0
Universal Adversarial Attack on Aligned Multimodal LLMs—0
Democratic Training Against Universal Adversarial Perturbations—0
Rigid Body Adversarial Attacks—0
BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing AttacksCode0
Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage—0
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction—0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement LearningCode0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection—0
Show:102550
← PrevPage 42 of 181Next →
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