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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 1441–1450 of 1808 papers

Title	Date	Tasks	Status	Hype
Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and Adverbs	Jan 20, 2022	Adversarial Attackautomatic short answer grading	CodeCode Available	0
Generating Natural Adversarial Examples	Oct 31, 2017	Adversarial Attackimage-classification	CodeCode Available	0
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency	Jul 1, 2019	Adversarial AttackClassification	CodeCode Available	0
Certified Defenses against Adversarial Examples	Jan 29, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
A practical approach to evaluating the adversarial distance for machine learning classifiers	Sep 5, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection	Jul 17, 2024	Adversarial Attack	CodeCode Available	0
Generating Textual Adversaries with Minimal Perturbation	Nov 12, 2022	Adversarial Attack	CodeCode Available	0
Generating Unrestricted 3D Adversarial Point Clouds	Nov 17, 2021	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0
CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack	Jun 1, 2025	Adversarial Attack	CodeCode Available	0
Adversarial attacks on neural networks through canonical Riemannian foliations	Mar 2, 2022	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