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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 1481–1490 of 1808 papers

Title	Date	Tasks	Status	Hype
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models	May 13, 2019	Adversarial Attack	CodeCode Available	0
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes	Feb 10, 2021	Adversarial AttackInference Attack	CodeCode Available	0
Bridging the Performance Gap between FGSM and PGD Adversarial Training	Nov 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking	May 13, 2025	Adversarial AttackAutonomous Driving	CodeCode Available	0
Adversarial Examples in Modern Machine Learning: A Review	Nov 13, 2019	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
An Empirical Investigation of Randomized Defenses against Adversarial Attacks	Sep 12, 2019	Adversarial AttackGeneral Classification	CodeCode Available	0
Trust Region Based Adversarial Attack on Neural Networks	Dec 16, 2018	Adversarial AttackGPU	CodeCode Available	0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition	Mar 22, 2023	Adversarial AttackAttribute	CodeCode Available	0
Heuristic Black-box Adversarial Attacks on Video Recognition Models	Nov 21, 2019	Adversarial AttackVideo Recognition	CodeCode Available	0
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting	Apr 22, 2021	Adversarial AttackAdversarial Robustness	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