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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 951–960 of 1808 papers

Title	Date	Tasks	Status	Hype
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis	Jan 13, 2022	Adversarial Attack	—Unverified	0
Towards Adversarially Robust Deep Image Denoising	Jan 12, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarially Robust Classification by Conditional Generative Model Inversion	Jan 12, 2022	Adversarial AttackClassification	—Unverified	0
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition	Jan 11, 2022	Adversarial AttackFace Recognition	CodeCode Available	0
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints	Jan 5, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes	Jan 4, 2022	Adversarial Attack	CodeCode Available	1
Towards Efficient Data Free Black-Box Adversarial Attack	Jan 1, 2022	Adversarial Attack	CodeCode Available	1
Bounded Adversarial Attack on Deep Content Features	Jan 1, 2022	Adversarial Attack	CodeCode Available	0
Exploring Effective Data for Surrogate Training Towards Black-Box Attack	Jan 1, 2022	Adversarial AttackDiversity	CodeCode Available	1
360-Attack: Distortion-Aware Perturbations From Perspective-Views	Jan 1, 2022	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