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 1701–1725 of 1808 papers

TitleStatusHype
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors—0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Trust Region Based Adversarial Attack on Neural NetworksCode0
Learning Transferable Adversarial Examples via Ghost NetworksCode0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
Fooling Network Interpretation in Image Classification—0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack—0
Prior Networks for Detection of Adversarial Attacks—0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous ApplicationsCode0
FineFool: Fine Object Contour Attack via Attention—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
A Frank-Wolfe Framework for Efficient and Effective Adversarial AttacksCode0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust AccuraciesCode0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial AttackCode0
Task-generalizable Adversarial Attack based on Perceptual MetricCode0
Intermediate Level Adversarial Attack for Enhanced Transferability—0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networksCode0
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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