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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 1281–1290 of 1808 papers

Title	Date	Tasks	Status	Hype
Semantically Stealthy Adversarial Attacks against Segmentation Models	Apr 5, 2021	Adversarial AttackSegmentation	—Unverified	0
Evaluating Neural Model Robustness for Machine Comprehension	Apr 1, 2021	Adversarial Attackmodel	—Unverified	0
Statistical inference for individual fairness	Mar 30, 2021	Adversarial AttackFairness	CodeCode Available	0
Robust Reinforcement Learning under model misspecification	Mar 29, 2021	Adversarial Attackmodel	CodeCode Available	0
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond	Mar 25, 2021	Adversarial AttackBeam Prediction	—Unverified	0
Vulnerability of Appearance-based Gaze Estimation	Mar 24, 2021	Adversarial AttackGaze Estimation	—Unverified	0
Grey-box Adversarial Attack And Defence For Sentiment Classification	Mar 22, 2021	Adversarial AttackClassification	CodeCode Available	0
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing	Mar 21, 2021	Adversarial Attacknamed-entity-recognition	—Unverified	0
Self adversarial attack as an augmentation method for immunohistochemical stainings	Mar 21, 2021	Adversarial AttackImage-to-Image Translation	—Unverified	0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack	Mar 19, 2021	Adversarial AttackComputational Efficiency	—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