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 921–930 of 1808 papers

TitleStatusHype
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
ImF: Implicit Fingerprint for Large Language Models—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary—0
A Generative Adversarial Attack for Multilingual Text Classifiers—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—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