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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 251–260 of 1808 papers

Title	Date	Tasks	Status	Hype
An Efficient Adversarial Attack for Tree Ensembles	Oct 22, 2020	Adversarial Attackvalid	CodeCode Available	1
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders	Oct 19, 2020	Adversarial AttackAlgorithmic Trading	CodeCode Available	1
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations	Oct 15, 2020	Adversarial Attack	CodeCode Available	1
Towards Resistant Audio Adversarial Examples	Oct 14, 2020	Adversarial Attackspeech-recognition	CodeCode Available	1
Adversarial Attack and Defense of Structured Prediction Models	Oct 4, 2020	Adversarial AttackDependency Parsing	CodeCode Available	1
Improving Query Efficiency of Black-box Adversarial Attack	Sep 24, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Examples in Deep Learning for Multivariate Time Series Regression	Sep 24, 2020	Adversarial AttackDeep Learning	CodeCode Available	1
Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations	Sep 21, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection	Sep 19, 2020	Adversarial AttackCo-Salient Object Detection	CodeCode Available	1
OpenAttack: An Open-source Textual Adversarial Attack Toolkit	Sep 19, 2020	Adversarial Attack	CodeCode Available	1

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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