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 251–275 of 1808 papers

TitleStatusHype
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustnessCode1
Improving Network Interpretability via Explanation Consistency Evaluation—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks—0
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability DetectionCode1
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous DrivingCode1
Compressed models are NOT miniature versions of large models—0
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift—0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent InfectionCode0
AEMIM: Adversarial Examples Meet Masked Image Modeling—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical AnalysisCode0
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks—0
Transferable 3D Adversarial Shape Completion using Diffusion ModelsCode0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition SystemsCode0
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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