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 1491–1500 of 1808 papers

TitleStatusHype
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty—0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
Towards Transferable Adversarial Attack against Deep Face Recognition—0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images—0
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen CamerasCode0
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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