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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 1071–1080 of 1808 papers

Title	Date	Tasks	Status	Hype
Protein Folding Neural Networks Are Not Robust	Sep 9, 2021	Adversarial AttackProtein Folding	—Unverified	0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning	Sep 8, 2021	Adversarial Attackcontinuous-control	—Unverified	0
Training Meta-Surrogate Model for Transferable Adversarial Attack	Sep 5, 2021	Adversarial Attackmodel	CodeCode Available	0
Real-World Adversarial Examples involving Makeup Application	Sep 4, 2021	Adversarial AttackFace Recognition	—Unverified	0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness	Sep 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models	Aug 29, 2021	Adversarial Attackreinforcement-learning	—Unverified	0
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks	Aug 29, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1
Disrupting Adversarial Transferability in Deep Neural Networks	Aug 27, 2021	Adversarial AttackFeature Correlation	CodeCode Available	0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE	Aug 25, 2021	Adversarial Attack	—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