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 281–290 of 1808 papers

TitleStatusHype
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets—0
L_p-norm Distortion-Efficient Adversarial Attack—0
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
EvolBA: Evolutionary Boundary Attack under Hard-label Black Box condition—0
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability—0
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
Deceptive Diffusion: Generating Synthetic Adversarial Examples—0
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features—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