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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 1731–1740 of 1808 papers

Title	Date	Tasks	Status	Hype
Improved Network Robustness with Adversary Critic	Oct 30, 2018	Adversarial Attack	CodeCode Available	0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Oct 24, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features	Oct 22, 2018	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
The UCR Time Series Archive	Oct 17, 2018	Adversarial AttackTime Series	CodeCode Available	0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only	Oct 10, 2018	Adversarial AttackQuantization	CodeCode Available	0
The Adversarial Attack and Detection under the Fisher Information Metric	Oct 9, 2018	Adversarial Attack	CodeCode Available	0
Improving the Generalization of Adversarial Training with Domain Adaptation	Oct 1, 2018	Adversarial AttackDomain Adaptation	CodeCode Available	0
CAAD 2018: Generating Transferable Adversarial Examples	Sep 29, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks	Sep 27, 2018	Adversarial Attackimage-classification	—Unverified	0
Universal Attacks on Equivariant Networks	Sep 27, 2018	Adversarial AttackTranslation	—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