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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 541–550 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge	May 26, 2021	Adversarial AttackGraph Embedding	—Unverified	0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech	Feb 13, 2025	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors	Mar 6, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
Adaptive Adversarial Attack on Scene Text Recognition	Jul 9, 2018	Adversarial Attackimage-classification	—Unverified	0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies	Aug 16, 2024	Adversarial AttackAdversarial Attack Detection	—Unverified	0
A Survey on Physical Adversarial Attacks against Face Recognition Systems	Oct 10, 2024	Adversarial AttackFace Recognition	—Unverified	0
A Survey on Physical Adversarial Attack in Computer Vision	Sep 28, 2022	Adversarial Attackobject-detection	—Unverified	0
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies	Mar 26, 2022	Adversarial Attack	—Unverified	0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers	Oct 4, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Adversarial Imitation Attack	Mar 28, 2020	Adversarial Attack	—Unverified	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