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 1051–1075 of 1808 papers

TitleStatusHype
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Sparse and Transferable Universal Singular Vectors Attack—0
SSCAE: A Novel Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator—0
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
STA: Adversarial Attacks on Siamese Trackers—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Stabilized Medical Attacks—0
Stabilizing Deep Tomographic Reconstruction—0
Standard detectors aren't (currently) fooled by physical adversarial stop signs—0
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning—0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Superclass Adversarial Attack—0
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation—0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks—0
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges—0
Suspicion-Free Adversarial Attacks on Clustering Algorithms—0
Graded Suspiciousness of Adversarial Texts to Human—0
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection—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