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 751–775 of 1808 papers

TitleStatusHype
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Defensive Quantization: When Efficiency Meets Robustness—0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples—0
Adversarial Attack with Raindrops—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
ExploreADV: Towards exploratory attack for Neural Networks—0
Generating Adversarial Examples with an Optimized Quality—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Exploring Adversarial Fake Images on Face Manifold—0
Exploring Adversarial Threat Models in Cyber Physical Battery Systems—0
Generating Unrestricted Adversarial Examples via Three Parameters—0
Exploring Frequency Adversarial Attacks for Face Forgery Detection—0
Exploring High-Order Structure for Robust Graph Structure Learning—0
Global Robustness Verification Networks—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models—0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images—0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—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