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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 631–640 of 1808 papers

Title	Date	Tasks	Status	Hype
Resilient Dynamic Average Consensus based on Trusted agents	Mar 14, 2023	Adversarial Attack	—Unverified	0
Constrained Adversarial Learning for Automated Software Testing: a literature review	Mar 14, 2023	Adversarial Attacksoftware testing	—Unverified	0
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?	Mar 13, 2023	Adversarial Attack	CodeCode Available	0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network	Mar 12, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality	Mar 12, 2023	Adversarial Attack	—Unverified	0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey	Mar 11, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Do we need entire training data for adversarial training?	Mar 10, 2023	Adversarial AttackSelf-Driving Cars	—Unverified	0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems	Mar 10, 2023	Adversarial AttackAutomatic Speech Recognition	—Unverified	0
Feature Unlearning for Pre-trained GANs and VAEs	Mar 10, 2023	Adversarial Attack	—Unverified	0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups	Mar 9, 2023	Adversarial AttackFairness	—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