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 601–610 of 1808 papers

TitleStatusHype
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
Trustworthy Actionable Perturbations—0
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors—0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Untargeted Adversarial Attack on Knowledge Graph Embeddings—0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—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