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 1501–1550 of 1808 papers

TitleStatusHype
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial AttackCode0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
Bounded Adversarial Attack on Deep Content FeaturesCode0
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen CamerasCode0
An Adversarial Attack Analysis on Malicious Advertisement URL Detection FrameworkCode0
Similarity-based Gray-box Adversarial Attack Against Deep Face RecognitionCode0
Spatial-Frequency Discriminability for Revealing Adversarial PerturbationsCode0
Identifying Adversarially Attackable and Robust SamplesCode0
Towards Analyzing Semantic Robustness of Deep Neural NetworksCode0
Towards Practical Robustness Analysis for DNNs based on PAC-Model LearningCode0
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF InfeasibleCode0
Simple and Efficient Partial Graph Adversarial Attack: A New PerspectiveCode0
Functional Adversarial AttacksCode0
Efficient Project Gradient Descent for Ensemble Adversarial AttackCode0
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack PerspectiveCode0
An Evasion Attack against Stacked Capsule AutoencoderCode0
Single-Class Target-Specific Attack against Interpretable Deep Learning SystemsCode0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
A Multi-task Adversarial Attack Against Face AuthenticationCode0
In-distribution adversarial attacks on object recognition models using gradient-free searchCode0
Efficient Formal Safety Analysis of Neural NetworksCode0
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement LearningCode0
Imperceptible Face Forgery Attack via Adversarial Semantic MaskCode0
Adversarial Attack for RGB-Event based Visual Object TrackingCode0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Provable defenses against adversarial examples via the convex outer adversarial polytopeCode0
Improved Network Robustness with Adversary CriticCode0
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function PriorCode0
HopSkipJumpAttack: A Query-Efficient Decision-Based AttackCode0
Efficient and Transferable Adversarial Examples from Bayesian Neural NetworksCode0
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion ModelsCode0
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed FeatureCode0
Robustness for Non-Parametric Classification: A Generic Attack and DefenseCode0
Pyramid Adversarial Training Improves ViT PerformanceCode0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition SystemsCode0
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and ChallengesCode0
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial ExamplesCode0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign ClassificationCode0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Query-Efficient Adversarial Attack Based on Latin Hypercube SamplingCode0
Dynamic Transformers Provide a False Sense of EfficiencyCode0
Improving the Generalization of Adversarial Training with Domain AdaptationCode0
Query-Efficient Black-box Adversarial Examples (superceded)Code0
Improving the robustness and accuracy of biomedical language models through adversarial trainingCode0
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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