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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 1741–1750 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack Type I: Cheat Classifiers by Significant Changes	Sep 3, 2018	Adversarial AttackVocal Bursts Type Prediction	—Unverified	0
Maximal Jacobian-based Saliency Map Attack	Aug 23, 2018	Adversarial AttackClassification	—Unverified	0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples	Aug 20, 2018	Adversarial AttackMetric Learning	—Unverified	0
Distributionally Adversarial Attack	Aug 16, 2018	Adversarial Attack	CodeCode Available	1
Structured Adversarial Attack: Towards General Implementation and Better Interpretability	Aug 5, 2018	Adversarial Attack	CodeCode Available	0
Rob-GAN: Generator, Discriminator, and Adversarial Attacker	Jul 27, 2018	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0
Evaluating and Understanding the Robustness of Adversarial Logit Pairing	Jul 26, 2018	Adversarial Attack	CodeCode Available	0
Harmonic Adversarial Attack Method	Jul 18, 2018	Adversarial Attack	—Unverified	0
With Friends Like These, Who Needs Adversaries?	Jul 11, 2018	Adversarial AttackClassification	CodeCode Available	0
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees	Jul 10, 2018	Adversarial AttackAdversarial Defense	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