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 1651–1675 of 1808 papers

TitleStatusHype
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
A Black-Box Attack on Optical Character Recognition Systems—0
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
A Robust Likelihood Model for Novelty Detection—0
ABIGX: A Unified Framework for eXplainable Fault Detection and Classification—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Defense Against Explanation Manipulation—0
Adversarial Attack on Sentiment Classification—0
Defense-guided Transferable Adversarial Attacks—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles—0
Defensive Quantization: When Efficiency Meets Robustness—0
SMART: Skeletal Motion Action Recognition aTtack—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
Democratic Training Against Universal Adversarial Perturbations—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Design of secure and robust cognitive system for malware detection—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