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 1651–1660 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
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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