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 751–800 of 1808 papers

TitleStatusHype
Model Leeching: An Extraction Attack Targeting LLMs—0
Transferable Adversarial Attack on Image Tampering Localization—0
Context-aware Adversarial Attack on Named Entity Recognition—0
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection—0
Outlier Robust Adversarial TrainingCode0
Adaptive Adversarial Training Does Not Increase Recourse Costs—0
MathAttack: Attacking Large Language Models Towards Math Solving Ability—0
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration—0
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models—0
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement LearningCode0
Can We Rely on AI?—0
A Classification-Guided Approach for Adversarial Attacks against Neural Machine TranslationCode0
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary—0
On-Manifold Projected Gradient Descent—0
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain DetectionCode0
Enhancing Adversarial Attacks: The Similar Target MethodCode0
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic GraphsCode0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification ModelsCode0
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery—0
Simple and Efficient Partial Graph Adversarial Attack: A New PerspectiveCode0
Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks—0
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook—0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning—0
Exploring the Physical World Adversarial Robustness of Vehicle Detection—0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks—0
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial AttackCode0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training—0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label LearningCode0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Adversarial Attacks on Traffic Sign Recognition: A Survey—0
On the Robustness of Split Learning against Adversarial Attacks—0
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks—0
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical WorldCode0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations—0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Single-Class Target-Specific Attack against Interpretable Deep Learning SystemsCode0
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-IdentificationCode0
Brightness-Restricted Adversarial Attack Patch—0
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—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