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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 111–120 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models	Nov 4, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification	Dec 21, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	1	5
3D Adversarial Attacks Beyond Point Cloud	Apr 25, 2021	Adversarial Attack	CodeCode Available	1	5
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1	5
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1	5
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1	5
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1	5
A Review of Adversarial Attack and Defense for Classification Methods	Nov 18, 2021	Adversarial AttackClassification	CodeCode Available	1	5

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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