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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 1751–1760 of 1808 papers

Title	Date	Tasks	Status	Hype
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
Adaptive Adversarial Attack on Scene Text Recognition	Jul 9, 2018	Adversarial Attackimage-classification	—Unverified	0
Adversarial Examples in Deep Learning: Characterization and Divergence	Jun 29, 2018	Adversarial AttackDeep Learning	—Unverified	0
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples	Jun 27, 2018	Adversarial AttackImage Captioning	CodeCode Available	0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System	Jun 23, 2018	Adversarial AttackFace Recognition	—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