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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 1411–1420 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition	Apr 4, 2022	Activity RecognitionAdversarial Attack	—Unverified	0	0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness	May 30, 2019	Adversarial Attack	—Unverified	0	0
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack	Feb 17, 2020	Adversarial Attack	—Unverified	0	0
Robust Superpixel-Guided Attentional Adversarial Attack	Jun 1, 2020	Adversarial AttackSteganalysis	—Unverified	0	0
Robust Text CAPTCHAs Using Adversarial Examples	Jan 7, 2021	Adversarial AttackOptical Character Recognition (OCR)	—Unverified	0	0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks	Jun 13, 2019	Adversarial AttackDeep Learning	—Unverified	0	0
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond	Mar 25, 2021	Adversarial AttackBeam Prediction	—Unverified	0	0
Robust Transfer Learning with Pretrained Language Models through Adapters	Aug 5, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Robust Unpaired Single Image Super-Resolution of Faces	Jan 22, 2022	Adversarial AttackImage Super-Resolution	—Unverified	0	0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective	Jan 21, 2022	Adversarial AttackGraph Learning	—Unverified	0	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