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 251–260 of 1808 papers

TitleStatusHype
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustnessCode1
Improving Network Interpretability via Explanation Consistency Evaluation—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks—0
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack—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