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 701–725 of 1808 papers

TitleStatusHype
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems—0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Enhancing Adversarial Transferability via Component-Wise Transformation—0
Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training—0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
A Study for Universal Adversarial Attacks on Texture Recognition—0
Global Robustness Verification Networks—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation—0
Enhancing the Transferability via Feature-Momentum Adversarial Attack—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
Defense-guided Transferable Adversarial Attacks—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Adversarial Attack with Pattern Replacement—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization—0
EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective—0
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features—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