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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 1141–1150 of 1808 papers

Title	Date	Tasks	Status	Hype
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation	Dec 3, 2023	Adversarial Attackimage-classification	—Unverified	0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients	Jan 1, 2023	Adversarial Attack	—Unverified	0
Transferable Adversarial Attack on Image Tampering Localization	Sep 19, 2023	Adversarial Attack	—Unverified	0
Transferable Adversarial Examples for Anchor Free Object Detection	Jun 3, 2021	Adversarial AttackObject	—Unverified	0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features	Oct 22, 2018	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Transferable Learned Image Compression-Resistant Adversarial Perturbations	Jan 6, 2024	Adversarial AttackAutonomous Driving	—Unverified	0
Transferable Perturbations of Deep Feature Distributions	Apr 27, 2020	Adversarial Attack	—Unverified	0
Transferable Physical Attack against Object Detection with Separable Attention	May 19, 2022	Adversarial Attackobject-detection	—Unverified	0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms	May 22, 2025	Adversarial AttackBenchmarking	—Unverified	0
Trustworthy Actionable Perturbations	May 18, 2024	Adversarial Attackcounterfactual	—Unverified	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