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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 1411–1420 of 1808 papers

Title	Date	Tasks	Status	Hype
Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script	Dec 3, 2024	Adversarial Attack	CodeCode Available	0
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples	Dec 21, 2023	Adversarial Attackcounterfactual	CodeCode Available	0
Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image	Apr 27, 2025	Adversarial Attack	CodeCode Available	0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack	Jun 3, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models	Jul 11, 2025	Adversarial Attack	CodeCode Available	0
The UCR Time Series Archive	Oct 17, 2018	Adversarial AttackTime Series	CodeCode Available	0
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0
A principled approach for generating adversarial images under non-smooth dissimilarity metrics	Aug 5, 2019	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