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 1026–1050 of 1808 papers

TitleStatusHype
Adversarial Attacks on ML Defense Models CompetitionCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
Adversarial Attack across Datasets—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
Identification of Attack-Specific Signatures in Adversarial Examples—0
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
Compressive Sensing Based Adaptive Defence Against Adversarial Images—0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
Adversarial Attack by Limited Point Cloud Surface Modifications—0
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Reversible Attack based on Local Visual Adversarial Perturbation—0
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based VisionCode0
Adversarial defenses via a mixture of generators—0
An Improved Genetic Algorithm and Its Application in Neural Network Adversarial AttackCode0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Linear Backpropagation Leads to Faster Convergence—0
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening—0
-Weighted Federated Adversarial Training—0
Adversarially Robust Conformal Prediction—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Pixab-CAM: Attend Pixel, not Channel—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