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

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Showing 501–550 of 1808 papers

TitleStatusHype
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection—0
Differentiable JPEG: The Devil is in the DetailsCode1
Outlier Robust Adversarial TrainingCode0
Certifying LLM Safety against Adversarial PromptingCode1
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
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model ModificationCode1
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic GraphsCode0
Enhancing Adversarial Attacks: The Similar Target MethodCode0
On the Adversarial Robustness of Multi-Modal Foundation ModelsCode1
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
AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery—0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification ModelsCode0
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
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed GradientCode1
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
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Multi-attacks: Many images + the same adversarial attack many target labelsCode1
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial AttackCode0
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
Universal and Transferable Adversarial Attacks on Aligned Language ModelsCode4
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated ExamplesCode1
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
Frequency Domain Adversarial Training for Robust Volumetric Medical SegmentationCode1
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations—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