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 401–425 of 1808 papers

TitleStatusHype
On the Multi-modal Vulnerability of Diffusion ModelsCode1
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
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
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
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision TransformersCode1
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles—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