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 76–100 of 1808 papers

TitleStatusHype
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Data-free Universal Adversarial Perturbation with Pseudo-semantic PriorCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Adversarial Ranking Attack and DefenseCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Show:102550
← PrevPage 4 of 73Next →
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