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 426–450 of 1808 papers

TitleStatusHype
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation—0
Attack Tree Analysis for Adversarial Evasion Attacks—0
Adversarial Attacks on Image Classification Models: Analysis and Defense—0
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial ExamplesCode0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Mutual-modality Adversarial Attack with Semantic Perturbation—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Towards Transferable Targeted 3D Adversarial Attack in the Physical WorldCode1
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
Towards Transferable Adversarial Attacks with Centralized Perturbation—0
Dynamic Adversarial Attacks on Autonomous Driving SystemsCode0
An adversarial attack approach for eXplainable AI evaluation on deepfake detection modelsCode0
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers—0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields—0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks—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