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

Showing 501525 of 1808 papers

TitleStatusHype
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
Certified Defenses against Adversarial ExamplesCode0
Fashion-Guided Adversarial Attack on Person SegmentationCode0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMsCode0
Robustness for Non-Parametric Classification: A Generic Attack and DefenseCode0
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and AdverbsCode0
Explainable Graph Neural Networks Under FireCode0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect PerspectiveCode0
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial TextsCode0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent InfectionCode0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial AttacksCode0
Exacerbating Algorithmic Bias through Fairness AttacksCode0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Evaluating the Validity of Word-level Adversarial Attacks with Large Language ModelsCode0
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent VariablesCode0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Adversarial Diffusion Attacks on Graph-based Traffic Prediction ModelsCode0
Adversarial Defense via Data Dependent Activation Function and Total Variation MinimizationCode0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial TrainingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified