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 1351–1400 of 1808 papers

TitleStatusHype
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
Adversarial Attack with Pattern Replacement—0
Rethinking Classifier and Adversarial Attack—0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes—0
Transferable Adversarial Examples for Anchor Free Object Detection—0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
Rethinking Textual Adversarial Defense for Pre-trained Language Models—0
Adaptive Adversarial Attack on Scene Text Recognition—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
RetouchUAA: Unconstrained Adversarial Attack via Image Retouching—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—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
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
Transferable Perturbations of Deep Feature Distributions—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
Transferable Physical Attack against Object Detection with Separable Attention—0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey—0
Rigid Body Adversarial Attacks—0
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—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
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks—0
Adversarial Attacks on Image Classification Models: Analysis and Defense—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
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution—0
Adversarial Attacks on Hidden Tasks in Multi-Task Learning—0
Activation Learning by Local Competitions—0
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization—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
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models—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