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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 621–630 of 1808 papers

Title	Date	Tasks	Status	Hype
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges	Mar 24, 2023	Adversarial AttackMedical Diagnosis	CodeCode Available	0
Feature Separation and Recalibration for Adversarial Robustness	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Semantic Image Attack for Visual Model Diagnosis	Mar 23, 2023	Adversarial AttackAttribute	—Unverified	0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems	Mar 22, 2023	Adversarial Attack	—Unverified	0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition	Mar 22, 2023	Adversarial AttackAttribute	CodeCode Available	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
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense	Mar 21, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Translate your gibberish: black-box adversarial attack on machine translation systems	Mar 20, 2023	Adversarial AttackMachine Translation	CodeCode Available	0
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models	Mar 18, 2023	Adversarial AttackBenchmarking	—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