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 501–525 of 1808 papers

TitleStatusHype
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
Understanding Model Ensemble in Transferable Adversarial Attack—0
Graded Suspiciousness of Adversarial Texts to Human—0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM InversionCode0
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack—0
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization—0
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations—0
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection—0
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image GenerationCode0
Cloud Adversarial Example Generation for Remote Sensing Image Classification—0
Hidden Activations Are Not Enough: A General Approach to Neural Network PredictionsCode0
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Deep generative models as an adversarial attack strategy for tabular machine learningCode0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective—0
XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution—0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—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