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 526–550 of 1808 papers

TitleStatusHype
Adversarial Attacks on Data AttributionCode0
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
One-Index Vector Quantization Based Adversarial Attack on Image Classification—0
Network transferability of adversarial patches in real-time object detectionCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models—0
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection SystemsCode0
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial AttacksCode0
Correlation Analysis of Adversarial Attack in Time Series Classification—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies—0
Evaluating the Validity of Word-level Adversarial Attacks with Large Language ModelsCode0
A Multi-task Adversarial Attack Against Face AuthenticationCode0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Improving Network Interpretability via Explanation Consistency Evaluation—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—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