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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 41–50 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models	Nov 4, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking	Mar 3, 2022	Adversarial AttackObject Tracking	CodeCode Available	1
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks	Feb 10, 2020	Adversarial AttackDeblurring	CodeCode Available	1
Adversarial Examples for Semantic Segmentation and Object Detection	Mar 24, 2017	Adversarial Attackimage-classification	CodeCode Available	1
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior	Feb 28, 2025	Adversarial Attack	CodeCode Available	1
Adversarial Learning for Robust Deep Clustering	Dec 1, 2020	Adversarial AttackClustering	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1
3D Gaussian Splat Vulnerabilities	May 30, 2025	3DGSAdversarial Attack	CodeCode Available	1

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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