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 876–900 of 1808 papers

TitleStatusHype
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
Harmonic Adversarial Attack Method—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
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Generalization to Mitigate Synonym Substitution Attacks—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Adversarial Attack on Sentiment Classification—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
A Black-Box Attack on Optical Character Recognition Systems—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—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