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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 171–180 of 1808 papers

Title	Date	Tasks	Status	Hype
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles	Jan 13, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
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
Exploring Effective Data for Surrogate Training Towards Black-Box Attack	Jan 1, 2022	Adversarial AttackDiversity	CodeCode Available	1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond	Jan 1, 2022	Adversarial AttackData Augmentation	CodeCode Available	1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art	Dec 23, 2021	Adversarial AttackMalware Detection	CodeCode Available	1
Triangle Attack: A Query-efficient Decision-based Adversarial Attack	Dec 13, 2021	Adversarial AttackDimensionality Reduction	CodeCode Available	1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness	Dec 5, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	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