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

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Showing 1501–1525 of 1808 papers

TitleStatusHype
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
Biologically inspired protection of deep networks from adversarial attacks—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Adversarial Attacks in Multimodal Systems: A Practitioner's Survey—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Black-box Adversarial ML Attack on Modulation Classification—0
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)—0
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness—0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples—0
Enhancing Transformation-based Defenses using a Distribution Classifier—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
Boosting Adversarial Transferability of MLP-Mixer—0
Boosting Adversarial Transferability through Enhanced Momentum—0
Boosting Adversarial Transferability using Dynamic Cues—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