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 581–590 of 1808 papers

TitleStatusHype
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization—0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Imperceptible Face Forgery Attack via Adversarial Semantic MaskCode0
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge GraphsCode0
Explainable Graph Neural Networks Under FireCode0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—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