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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 571–580 of 1808 papers

Title	Date	Tasks	Status	Hype
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers	Jun 8, 2023	Adversarial AttackFederated Learning	—Unverified	0
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach	Jun 6, 2023	Adversarial AttackColor Image Denoising	CodeCode Available	0
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified	0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception	Jun 5, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations	Jun 5, 2023	Adversarial Attack	CodeCode Available	0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy	Jun 4, 2023	Adversarial AttackAttribute	—Unverified	0
Adversarial Attack Based on Prediction-Correction	Jun 2, 2023	Adversarial AttackPrediction	—Unverified	0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach	Jun 1, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search	Jun 1, 2023	Adversarial AttackEfficient Exploration	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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