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 421–430 of 1808 papers

TitleStatusHype
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology ImagingCode0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision TransformersCode1
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles—0
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation—0
Attack Tree Analysis for Adversarial Evasion Attacks—0
Adversarial Attacks on Image Classification Models: Analysis and Defense—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