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 871–880 of 1808 papers

TitleStatusHype
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
An Adversarial Approach to Evaluating the Robustness of Event Identification Models—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—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