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 201–250 of 1808 papers

TitleStatusHype
Transferable Adversarial Attacks on SAM and Its Downstream ModelsCode1
Adversarial Attacks on Large Language Models Using Regularized RelaxationCode0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language ModelsCode0
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
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations—0
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization—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
Deep generative models as an adversarial attack strategy for tabular machine learningCode0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition—0
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
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
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
Adversarial Attacks on Data AttributionCode0
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
One-Index Vector Quantization Based Adversarial Attack on Image Classification—0
Network transferability of adversarial patches in real-time object detectionCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models—0
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection SystemsCode0
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial AttacksCode0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
Correlation Analysis of Adversarial Attack in Time Series Classification—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—0
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein modelCode1
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies—0
Evaluating the Validity of Word-level Adversarial Attacks with Large Language ModelsCode0
A Multi-task Adversarial Attack Against Face AuthenticationCode0
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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