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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 991–1000 of 1808 papers

Title	Date	Tasks	Status	Hype
A Practical and Stealthy Adversarial Attack for Cyber-Physical Applications	Nov 22, 2021	Adversarial Attack	—Unverified	0
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability	Nov 21, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1
Fooling Adversarial Training with Inducing Noise	Nov 19, 2021	Adversarial Attack	—Unverified	0
Enhanced countering adversarial attacks via input denoising and feature restoring	Nov 19, 2021	Adversarial AttackDenoising	CodeCode Available	0
A Review of Adversarial Attack and Defense for Classification Methods	Nov 18, 2021	Adversarial AttackClassification	CodeCode Available	1
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers	Nov 17, 2021	Adversarial AttackMulti-Object Tracking	CodeCode Available	1
Generating Unrestricted 3D Adversarial Point Clouds	Nov 17, 2021	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting	Nov 16, 2021	Adversarial AttackGrammatical Error Correction	—Unverified	0
Input-specific Attention Subnetworks for Adversarial Detection	Nov 16, 2021	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