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 1101–1125 of 1808 papers

TitleStatusHype
Model Leeching: An Extraction Attack Targeting LLMs—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Moving Target Defense Against Adversarial False Data Injection Attacks In Power Grids—0
MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification—0
MultAV: Multiplicative Adversarial Videos—0
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition—0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation—0
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models—0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Towards Building a Robust Toxicity Predictor—0
Multi-head Uncertainty Inference for Adversarial Attack Detection—0
Towards Calibration Enhanced Network by Inverse Adversarial Attack—0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations—0
Multi-step domain adaptation by adversarial attack to H ΔH-divergence—0
Multi-Task Adversarial Attack—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Mutual-modality Adversarial Attack with Semantic Perturbation—0
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK—0
Towards Certified Defense for Unrestricted Adversarial Attacks—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—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