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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 811–820 of 1808 papers

Title	Date	Tasks	Status	Hype
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models	Jun 9, 2023	Adversarial Attack	—Unverified	0
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning	Jun 8, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers	Jun 8, 2023	Adversarial AttackFederated Learning	—Unverified	0
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified	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
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations	Jun 5, 2023	Adversarial Attack	CodeCode Available	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
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

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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