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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 1391–1400 of 1808 papers

Title	Date	Tasks	Status	Hype
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability	Oct 14, 2020	Adversarial Attacktext-classification	CodeCode Available	0
An Evasion Attack against Stacked Capsule Autoencoder	Oct 14, 2020	Adversarial Attackimage-classification	CodeCode Available	0
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack	Oct 14, 2020	Adversarial Attack	CodeCode Available	0
An Analysis of Robustness of Non-Lipschitz Networks	Oct 13, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	0
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks	Oct 13, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
EFSG: Evolutionary Fooling Sentences Generator	Oct 12, 2020	Adversarial AttackBinary Classification	—Unverified	0
Learning Task-aware Robust Deep Learning Systems	Oct 11, 2020	Adversarial AttackDeep Learning	—Unverified	0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems	Oct 7, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial attacks on audio source separation	Oct 7, 2020	Adversarial AttackAudio Source Separation	—Unverified	0
Adversarial Patch Attacks on Monocular Depth Estimation Networks	Oct 6, 2020	Adversarial AttackDepth Estimation	—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