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

Showing 176200 of 1808 papers

TitleStatusHype
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
Frequency-driven Imperceptible Adversarial Attack on Semantic SimilarityCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
A Survey On Universal Adversarial AttackCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
R&R: Metric-guided Adversarial Sentence GenerationCode1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
GreedyFool: Distortion-Aware Sparse Adversarial AttackCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
BayesOpt Adversarial AttackCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified