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 776–800 of 1808 papers

TitleStatusHype
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Defensive Quantization: When Efficiency Meets Robustness—0
Adversarial Attack with Raindrops—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Defense-guided Transferable Adversarial Attacks—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Adversarial Attack with Pattern Replacement—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Global Robustness Verification Networks—0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN—0
Defense Against Explanation Manipulation—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks—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