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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 1541–1550 of 1808 papers

Title	Date	Tasks	Status	Hype
Improving Robustness of Task Oriented Dialog Systems	Nov 12, 2019	Adversarial AttackData Augmentation	—Unverified	0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN	Nov 12, 2019	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy	Nov 10, 2019	Adversarial AttackAtari Games	—Unverified	0
Patch augmentation: Towards efficient decision boundaries for neural networks	Nov 8, 2019	Adversarial Attack	CodeCode Available	0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems	Nov 8, 2019	Adversarial AttackIntrusion Detection	—Unverified	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

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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