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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 1431–1440 of 1808 papers

Title	Date	Tasks	Status	Hype
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation	Nov 16, 2022	Adversarial AttackDiversity	CodeCode Available	0
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components	Dec 1, 2019	Adversarial AttackClassification	CodeCode Available	0
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models	Aug 15, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	0
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain	May 19, 2019	Adversarial Attack	CodeCode Available	0
Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems	May 14, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Generate synthetic samples from tabular data	Sep 12, 2022	Adversarial Attack	CodeCode Available	0
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images	Feb 13, 2023	Adversarial AttackObject	CodeCode Available	0
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense	Mar 19, 2022	Adversarial Attack	CodeCode Available	0
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests	Feb 7, 2022	Adversarial AttackVocal Bursts Valence Prediction	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