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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 241–250 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification	Dec 21, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	1	5
Rethinking Image Restoration for Object Detection	Nov 1, 2022	Adversarial AttackDomain Adaptation	CodeCode Available	1	5
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	May 7, 2021	Adversarial AttackFace Generation	CodeCode Available	1	5
Deep Variational Information Bottleneck	Dec 1, 2016	Adversarial Attack	CodeCode Available	1	5
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack	Jun 1, 2020	Adversarial AttackBinarization	CodeCode Available	1	5
Robust Deep Reinforcement Learning through Adversarial Loss	Aug 5, 2020	Adversarial AttackAtari Games	CodeCode Available	1	5
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1	5
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC	Mar 18, 2022	Adversarial Attack	CodeCode Available	1	5
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks	Aug 12, 2022	Adversarial Attack	CodeCode Available	1	5
Disentangled Information Bottleneck	Dec 14, 2020	Adversarial AttackOut-of-Distribution Detection	CodeCode Available	1	5

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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