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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 1121–1130 of 1808 papers

Title	Date	Tasks	Status	Hype
Fooling Network Interpretation in Image Classification	Dec 6, 2018	Adversarial AttackClassification	—Unverified	0
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning	Nov 14, 2021	Adversarial Attack	—Unverified	0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack	Dec 6, 2018	Adversarial Attack	—Unverified	0
Towards more transferable adversarial attack in black-box manner	May 23, 2025	Adversarial AttackAdversarial Purification	—Unverified	0
Towards Natural Robustness Against Adversarial Examples	Dec 4, 2020	Adversarial Attack	—Unverified	0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks	Apr 11, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards Security Threats of Deep Learning Systems: A Survey	Nov 28, 2019	Adversarial AttackDeep Learning	—Unverified	0
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks	Feb 18, 2025	Adversarial AttackAutonomous Vehicles	—Unverified	0
Towards Robustness of Deep Neural Networks via Regularization	Jan 1, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning	Dec 16, 2021	Adversarial AttackImage Compression	—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