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 401–450 of 1808 papers

TitleStatusHype
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
Benchmarking Transferable Adversarial AttacksCode1
AdvGPS: Adversarial GPS for Multi-Agent Perception AttackCode0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial AttacksCode2
Sparse and Transferable Universal Singular Vectors Attack—0
Fluent dreaming for language modelsCode1
Exploring Adversarial Threat Models in Cyber Physical Battery Systems—0
Susceptibility of Adversarial Attack on Medical Image Segmentation ModelsCode0
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color AttackCode0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems—0
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
A Generative Adversarial Attack for Multilingual Text Classifiers—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
Data-Driven Subsampling in the Presence of an Adversarial ActorCode0
Transferable Learned Image Compression-Resistant Adversarial Perturbations—0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology ImagingCode0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision TransformersCode1
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles—0
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