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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 431–440 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	0	5
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning	May 5, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	0	5
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting	Oct 20, 2023	Adversarial AttackGrammatical Error Correction	CodeCode Available	0	5
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	0	5
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available	0	5
Improved Network Robustness with Adversary Critic	Oct 30, 2018	Adversarial Attack	CodeCode Available	0	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0	5

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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