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 226–250 of 1808 papers

TitleStatusHype
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models—0
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
Query-Efficient Video Adversarial Attack with Stylized Logo—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial AttacksCode0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
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
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein modelCode1
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
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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