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 1226–1250 of 1808 papers

TitleStatusHype
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
Random Transformation of Image Brightness for Adversarial AttackCode0
Exploring Adversarial Fake Images on Face Manifold—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
Robust Text CAPTCHAs Using Adversarial Examples—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Towards Robustness of Deep Neural Networks via Regularization—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Adversarial Example Detection Using Latent Neighborhood Graph—0
Stabilized Medical Attacks—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
An Adversarial Attack via Feature Contributive Regions—0
Practical Order Attack in Deep Ranking—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Patch-wise++ Perturbation for Adversarial Targeted AttacksCode1
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Sparse Adversarial Attack to Object DetectionCode1
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—0
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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