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 1026–1050 of 1808 papers

TitleStatusHype
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Semantic Image Attack for Visual Model Diagnosis—0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm—0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks—0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models—0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Sequential Attacks on Agents for Long-Term Adversarial Goals—0
SHARP: Search-Based Adversarial Attack for Structured Prediction—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Should Adversarial Attacks Use Pixel p-Norm?—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Simple and Effective Stochastic Neural Networks—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
SMART: Skeletal Motion Action Recognition aTtack—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
Snowball Adversarial Attack on Traffic Sign Classification—0
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method—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