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 1151–1200 of 1808 papers

TitleStatusHype
Input-specific Attention Subnetworks for Adversarial Detection—0
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning—0
Defense Against Explanation Manipulation—0
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
Disrupting Deep Uncertainty Estimation Without Harming AccuracyCode0
Generating Watermarked Adversarial Texts—0
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust—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
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction ModelsCode0
Adversarial Attacks on Gaussian Process BanditsCode0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
Identification of Attack-Specific Signatures in Adversarial Examples—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
Adversarial Attack across Datasets—0
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
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based VisionCode0
Reversible Attack based on Local Visual Adversarial Perturbation—0
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
An Improved Genetic Algorithm and Its Application in Neural Network Adversarial AttackCode0
Adversarial defenses via a mixture of generators—0
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Fooling Adversarial Training with Induction Noise—0
-Weighted Federated Adversarial Training—0
One for Many: an Instagram inspired black-box adversarial attack—0
Linear Backpropagation Leads to Faster Convergence—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Adversarially Robust Conformal Prediction—0
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Pixab-CAM: Attend Pixel, not Channel—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial AttackCode0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Robust Physical-World Attacks on Face Recognition—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—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