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 851–875 of 1808 papers

TitleStatusHype
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing—0
Generating Watermarked Adversarial Texts—0
Generating Valid and Natural Adversarial Examples with Large Language Models—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Generating Unrestricted Adversarial Examples via Three Parameters—0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Adversarial Relighting Against Face Recognition—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Benchmarking Adversarial Robustness—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Generating Adversarial Examples with an Optimized Quality—0
Generating Adversarial Attacks in the Latent Space—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Adversarial Attack on Skeleton-based Human Action Recognition—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