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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 1521–1530 of 1808 papers

Title	Date	Tasks	Status	Hype
Undersensitivity in Neural Reading Comprehension	Feb 15, 2020	Adversarial AttackData Augmentation	—Unverified	0
Adversarial Data Encryption	Feb 10, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels	Feb 5, 2020	Adversarial Attack	—Unverified	0
DANCE: Enhancing saliency maps using decoys	Feb 3, 2020	Adversarial Attack	CodeCode Available	0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier	Jan 27, 2020	Adversarial AttackMalware Detection	—Unverified	0
Analyzing the Noise Robustness of Deep Neural Networks	Jan 26, 2020	Adversarial Attack	—Unverified	0
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning	Jan 21, 2020	Adversarial AttackDeep Learning	—Unverified	0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet	Jan 16, 2020	Adversarial Attack	—Unverified	0
Generating Semantic Adversarial Examples via Feature Manipulation	Jan 6, 2020	Adversarial AttackDisentanglement	—Unverified	0
Interpolation between CNNs and ResNets	Jan 1, 2020	Adversarial Attackimage-classification	—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