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 1001–1050 of 1808 papers

TitleStatusHype
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks—0
Improving the robustness and accuracy of biomedical language models through adversarial trainingCode0
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning—0
Sparse Adversarial Video Attacks with Spatial TransformationsCode1
Defense Against Explanation Manipulation—0
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Attack against Cross-lingual Knowledge Graph Alignment—0
An Actor-Critic Method for Simulation-Based Optimization—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack FrameworkCode1
Disrupting Deep Uncertainty Estimation Without Harming AccuracyCode0
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust—0
Generating Watermarked Adversarial Texts—0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information—0
Boosting the Transferability of Video Adversarial Examples via Temporal TranslationCode1
Unrestricted Adversarial Attacks on ImageNet CompetitionCode1
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction ModelsCode0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Adversarial Attacks on Gaussian Process BanditsCode0
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