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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 1561–1570 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems	Nov 8, 2019	Adversarial AttackIntrusion Detection	—Unverified	0
Patch augmentation: Towards efficient decision boundaries for neural networks	Nov 8, 2019	Adversarial Attack	CodeCode Available	0
White-Box Target Attack for EEG-Based BCI Regression Problems	Nov 7, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Reversible Adversarial Attack based on Reversible Image Transformation	Nov 6, 2019	Adversarial AttackImage Restoration	—Unverified	0
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems	Nov 3, 2019	Adversarial AttackSpeaker Recognition	CodeCode Available	0
The FEVER2.0 Shared Task	Nov 1, 2019	Adversarial Attack	—Unverified	0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System	Oct 31, 2019	Adversarial AttackReal-World Adversarial Attack	—Unverified	0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization	Oct 30, 2019	Adversarial AttackIntrusion Detection	—Unverified	0
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks	Oct 29, 2019	Adversarial AttackUncertainty Quantification	CodeCode Available	0
Word-level Textual Adversarial Attacking as Combinatorial Optimization	Oct 27, 2019	Adversarial AttackCombinatorial Optimization	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