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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1571–1580 of 1808 papers

Title	Date	Tasks	Status	Hype
Dynamics-aware Adversarial Attack of Adaptive Neural Networks	Oct 15, 2022	Adversarial AttackComputational Efficiency	CodeCode Available	0
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds	Dec 1, 2019	Adversarial AttackClassify 3D Point Clouds	CodeCode Available	0
Real-Time Adversarial Attacks	May 31, 2019	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
AdvHat: Real-world adversarial attack on ArcFace Face ID system	Aug 23, 2019	Adversarial AttackReal-World Adversarial Attack	CodeCode Available	0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method	Jan 26, 2021	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Uncertainty Estimation of Transformer Predictions for Misclassification Detection	May 1, 2022	Active LearningAdversarial Attack	CodeCode Available	0
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models	Apr 19, 2021	Adversarial AttackManagement	CodeCode Available	0
Real-world adversarial attack on MTCNN face detection system	Oct 14, 2019	Adversarial Attack	CodeCode Available	0
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients	Mar 5, 2025	Adversarial Attack	CodeCode Available	0
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis	Jul 16, 2024	Adversarial Attack	CodeCode Available	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified