An Improved LLE Algorithm with Sparse Constraint

Ye, Qingwei; Sun, Yang; Wang, Xiaodong; Yu, Zhou

doi:10.1166/jctn.2013.3293

Cited by 5 publications

(1 citation statement)

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“…The second module focuses on detecting facial landmarks that are not obscured by occlusion. The LLE algorithm is implemented at this stage [ 50 ]. In the final module, classification and regression tasks are carried out using a CNN to determine if an item is a face and to scale the position of missing facial signals.…”

Section: Related Workmentioning

confidence: 99%

Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system

Sheikh

Zafar

2023

Multimed Tools Appl

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The face mask detection system has been a valuable tool to combat COVID-19 by preventing its rapid transmission. This article demonstrated that the present deep learning-based face mask detection systems are vulnerable to adversarial attacks. We proposed a framework for a robust face mask detection system that is resistant to adversarial attacks. We first developed a face mask detection system by fine-tuning the MobileNetv2 model and training it on the custom-built dataset. The model performed exceptionally well, achieving 95.83% of accuracy on test data. Then, the model’s performance is assessed using adversarial images calculated by the fast gradient sign method (FGSM). The FGSM attack reduced the model’s classification accuracy from 95.83% to 14.53%, indicating that the adversarial attack on the proposed model severely damaged its performance. Finally, we illustrated that the proposed robust framework enhanced the model's resistance to adversarial attacks. Although there was a notable drop in the accuracy of the robust model on unseen clean data from 95.83% to 92.79%, the model performed exceptionally well, improving the accuracy from 14.53% to 92% on adversarial data. We expect our research to heighten awareness of adversarial attacks on COVID-19 monitoring systems and inspire others to protect healthcare systems from similar attacks.

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Section: Related Workmentioning

confidence: 99%

Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system

Sheikh

Zafar

2023

Multimed Tools Appl

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Locality constrained dictionary learning for non‐linear dimensionality reduction and classification

Liu

Ling

et al. 2016

IET Computer Vision

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In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non‐linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub‐dictionaries are class‐specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large‐scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors’ algorithm outperforms some competing dictionary learning methods.

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Compression Sampling System of Vibration Signal Based on Sparse AR Model

Cheng¹,

Wang³

2014

J. Inf. Comput. Sci.

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An Improved LLE Algorithm with Sparse Constraint

Cited by 5 publications

References 0 publications

Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system

Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system

Locality constrained dictionary learning for non‐linear dimensionality reduction and classification

Compression Sampling System of Vibration Signal Based on Sparse AR Model

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