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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 1801–1808 of 1808 papers

Title	Date	Tasks	Status
Adversarial Images for Variational Autoencoders	Dec 1, 2016	Adversarial Attack	CodeCode Available
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions	Nov 17, 2016	Adversarial AttackMalware Detection	—Unverified
Delving into Transferable Adversarial Examples and Black-box Attacks	Nov 8, 2016	Adversarial AttackAdversarial Defense	CodeCode Available
Safety Verification of Deep Neural Networks	Oct 21, 2016	Adversarial AttackAdversarial Defense	CodeCode Available
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library	Oct 3, 2016	Adversarial AttackAdversarial Defense	CodeCode Available
Towards Evaluating the Robustness of Neural Networks	Aug 16, 2016	Adversarial Attack	CodeCode Available
The Limitations of Deep Learning in Adversarial Settings	Nov 24, 2015	Adversarial AttackAdversarial Defense	CodeCode Available
DeepFool: a simple and accurate method to fool deep neural networks	Nov 14, 2015	Adversarial AttackGeneral Classification	CodeCode Available

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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