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 1641–1650 of 1808 papers

TitleStatusHype
Universal Adversarial Perturbations and Image Spam Classifiers—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—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