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 1601–1625 of 1808 papers

TitleStatusHype
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models—0
CorrAttack: Black-box Adversarial Attack with Structured Search—0
Correlation Analysis of Adversarial Attack in Time Series Classification—0
Corruption Robust Offline Reinforcement Learning with Human Feedback—0
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection—0
A Study for Universal Adversarial Attacks on Texture Recognition—0
Should Adversarial Attacks Use Pixel p-Norm?—0
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust—0
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models—0
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness—0
Universal Adversarial Attack on Aligned Multimodal LLMs—0
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization—0
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense—0
DA^3: A Distribution-Aware Adversarial Attack against Language Models—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—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