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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 861–870 of 1808 papers

Title	Date	Tasks	Status	Hype
Btech thesis report on adversarial attack detection and purification of adverserially attacked images	May 9, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems	May 5, 2022	Adversarial AttackKnowledge Distillation	—Unverified	0
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
BERTops: Studying BERT Representations under a Topological Lens	May 2, 2022	Adversarial Attack	CodeCode Available	0
Deep-Attack over the Deep Reinforcement Learning	May 2, 2022	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Uncertainty Estimation of Transformer Predictions for Misclassification Detection	May 1, 2022	Active LearningAdversarial Attack	CodeCode Available	0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions	May 1, 2022	Adversarial AttackCombinatorial Optimization	CodeCode Available	1
Adversarial attacks on an optical neural network	Apr 29, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—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