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 676–700 of 1808 papers

TitleStatusHype
Susceptibility of Adversarial Attack on Medical Image Segmentation ModelsCode0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color AttackCode0
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems—0
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
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
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
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial ExamplesCode0
Mutual-modality Adversarial Attack with Semantic Perturbation—0
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
Towards Transferable Adversarial Attacks with Centralized Perturbation—0
Show:102550
← PrevPage 28 of 73Next →
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