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 51–75 of 1808 papers

TitleStatusHype
Data-Driven Falsification of Cyber-Physical SystemsCode0
Adversarial Attacks in Multimodal Systems: A Practitioner's Survey—0
Adversarial Robustness Analysis of Vision-Language Models in Medical Image SegmentationCode0
Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—0
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—0
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single ImageCode0
Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
Adversarial Attack for RGB-Event based Visual Object TrackingCode0
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation—0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign ClassificationCode0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models—0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale DatasetCode0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Towards Calibration Enhanced Network by Inverse Adversarial Attack—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Moving Target Defense Against Adversarial False Data Injection Attacks In Power Grids—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification—0
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks—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