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 551–575 of 1808 papers

TitleStatusHype
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors—0
Adaptive Adversarial Attack on Scene Text Recognition—0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
A Survey on Physical Adversarial Attack in Computer Vision—0
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Adversarial Imitation Attack—0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
A Study for Universal Adversarial Attacks on Texture Recognition—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—0
Fooling the primate brain with minimal, targeted image manipulation—0
A Bayes-Optimal View on Adversarial Examples—0
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?—0
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems—0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
A Robust Likelihood Model for Novelty Detection—0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—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