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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 31–40 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Learning for Robust Deep Clustering	Dec 1, 2020	Adversarial AttackClustering	CodeCode Available	1	5
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	CodeCode Available	1	5
Adversarial Attack and Defense in Deep Ranking	Jun 7, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Adversarial Attack and Defense of Structured Prediction Models	Oct 4, 2020	Adversarial AttackDependency Parsing	CodeCode Available	1	5
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios	Feb 10, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior	Feb 28, 2025	Adversarial Attack	CodeCode Available	1	5
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink	Mar 11, 2021	Adversarial Attack	CodeCode Available	1	5
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	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