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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 851–860 of 1808 papers

Title	Date	Tasks	Status	Hype
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack	Apr 8, 2023	Adversarial AttackAttribute	—Unverified	0
GradMDM: Adversarial Attack on Dynamic Networks	Apr 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0
To be Robust and to be Fair: Aligning Fairness with Robustness	Mar 31, 2023	Adversarial AttackFairness	—Unverified	0
Class-Conditioned Transformation for Enhanced Robust Image Classification	Mar 27, 2023	Adversarial AttackClassification	CodeCode Available	0
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Effective black box adversarial attack with handcrafted kernels	Mar 24, 2023	Adversarial Attack	—Unverified	0
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges	Mar 24, 2023	Adversarial AttackMedical Diagnosis	CodeCode Available	0
Semantic Image Attack for Visual Model Diagnosis	Mar 23, 2023	Adversarial AttackAttribute	—Unverified	0
Revisiting DeepFool: generalization and improvement	Mar 22, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Wasserstein Adversarial Examples on Univariant Time Series Data	Mar 22, 2023	Adversarial AttackTime Series	—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