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 151–200 of 1808 papers

TitleStatusHype
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin StatisticCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social NetworksCode1
Smart App Attack: Hacking Deep Learning Models in Android AppsCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
StyleFool: Fooling Video Classification Systems via Style TransferCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Alleviating Adversarial Attacks on Variational Autoencoders with MCMCCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
Frequency-driven Imperceptible Adversarial Attack on Semantic SimilarityCode1
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural PhenomenonCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Random Walks for Adversarial MeshesCode1
Universal Adversarial Examples in Remote Sensing: Methodology and BenchmarkCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) frameworkCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
Towards Transferable Unrestricted Adversarial Examples with Minimum ChangesCode1
Towards Efficient Data Free Black-Box Adversarial AttackCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Triangle Attack: A Query-efficient Decision-based Adversarial AttackCode1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial RobustnessCode1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial TransferabilityCode1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking TrackersCode1
Sparse Adversarial Video Attacks with Spatial TransformationsCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack FrameworkCode1
Boosting the Transferability of Video Adversarial Examples via Temporal TranslationCode1
Unrestricted Adversarial Attacks on ImageNet CompetitionCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial AttackCode1
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification ModelsCode1
Multi-granularity Textual Adversarial Attack with Behavior CloningCode1
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural NetworksCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
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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