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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 1041–1050 of 1808 papers

Title	Date	Tasks	Status	Hype
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
Deep-Attack over the Deep Reinforcement Learning	May 2, 2022	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
BERTops: Studying BERT Representations under a Topological Lens	May 2, 2022	Adversarial Attack	CodeCode Available	0
Uncertainty Estimation of Transformer Predictions for Misclassification Detection	May 1, 2022	Active LearningAdversarial Attack	CodeCode Available	0
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
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework	Apr 27, 2022	Adversarial Attack	CodeCode Available	0
Mixed Strategies for Security Games with General Defending Requirements	Apr 26, 2022	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