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
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Adaptive Perturbation for Adversarial Attack—0
A White-Box Adversarial Attack Against a Digital Twin—0
AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Bandlimiting Neural Networks Against Adversarial Attacks—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Certifiably Robust Variational Autoencoders—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
CharBot: A Simple and Effective Method for Evading DGA Classifiers—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Benchmarking Adversarial Robustness—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Automated Decision-based Adversarial Attacks—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—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