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

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Showing 501–550 of 1808 papers

TitleStatusHype
Query-Efficient Black-box Adversarial Examples (superceded)Code0
Certified Defenses against Adversarial ExamplesCode0
A principled approach for generating adversarial images under non-smooth dissimilarity metricsCode0
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkCode0
Rob-GAN: Generator, Discriminator, and Adversarial AttackerCode0
Real-Time Adversarial AttacksCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Geometry-Aware Generation of Adversarial Point CloudsCode0
Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and AdverbsCode0
Robustness for Non-Parametric Classification: A Generic Attack and DefenseCode0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single ImageCode0
Foiling Explanations in Deep Neural NetworksCode0
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMsCode0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent InfectionCode0
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation TechniquesCode0
FireBERT: Hardening BERT-based classifiers against adversarial attackCode0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
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
Feature Space Perturbations Yield More Transferable Adversarial ExamplesCode0
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural NetworksCode0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
Angelic Patches for Improving Third-Party Object Detector PerformanceCode0
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial TextsCode0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
A New Ensemble Adversarial Attack Powered by Long-term Gradient MemoriesCode0
Fashion-Guided Adversarial Attack on Person SegmentationCode0
Fast Inference of Removal-Based Node InfluenceCode0
Explainable Graph Neural Networks Under FireCode0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect PerspectiveCode0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent VariablesCode0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial AttacksCode0
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and ChallengesCode0
Exacerbating Algorithmic Bias through Fairness AttacksCode0
Excess Capacity and Backdoor PoisoningCode0
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
A Classification-Guided Approach for Adversarial Attacks against Neural Machine TranslationCode0
Evaluating and Understanding the Robustness of Adversarial Logit PairingCode0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
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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