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 951–1000 of 1808 papers

TitleStatusHype
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack—0
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation—0
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping—0
Retention Score: Quantifying Jailbreak Risks for Vision Language Models—0
Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective—0
Rethinking Adversarial Transferability from a Data Distribution Perspective—0
Rethinking Classifier and Adversarial Attack—0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness—0
Rethinking Textual Adversarial Defense for Pre-trained Language Models—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Reversible Attack based on Local Visual Adversarial Perturbation—0
Reversible Adversarial Attack based on Reversible Image Transformation—0
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
Rigid Body Adversarial Attacks—0
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous—0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting—0
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors—0
Robust Certification for Laplace Learning on Geometric Graphs—0
Robust Constrained Reinforcement Learning—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks—0
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey—0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—0
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks—0
Robustness of Explanation Methods for NLP Models—0
Testing robustness of predictions of trained classifiers against naturally occurring perturbations—0
Robustness of Selected Learning Models under Label-Flipping Attack—0
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach—0
Robust Physical-World Attacks on Face Recognition—0
Robust saliency maps with distribution-preserving decoys—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness—0
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
Robust Text CAPTCHAs Using Adversarial Examples—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Robust Unpaired Single Image Super-Resolution of Faces—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp—0
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints—0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
SAD: Saliency-based Defenses Against Adversarial Examples—0
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors—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