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 76100 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
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution LearningCode1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
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
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
Fooling the Image Dehazing Models by First Order GradientCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
BayesOpt Adversarial AttackCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
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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