Face anti-spoofing using feature distilling and global attention learning

Huang, Rui; Wang, Xin

doi:10.1016/j.patcog.2022.109147

Cited by 12 publications

(2 citation statements)

References 18 publications

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“…Gender bias in research and clinical trials has been reported where male-centric research is given more funding compared to female-centric research [137,138]. There have been attempts to mitigate these biases [139]. Addressing these issues in the biotechnology Industry 5.0, which includes the implicit bias in ML systems, will result in a secure and privacy-oriented Industry 5.0.…”

Section: Ai Ethics In Biotechnology: Addressing Potential Biases and ...mentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies such as Machine Learning (ML), the Internet of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective on how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceutical sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in the biotechnology Industry 5.0.

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Section: Ai Ethics In Biotechnology: Addressing Potential Biases and ...mentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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show abstract

“…A study [17] developed a FAS Net network framework that uses a pre-trained Convolutional Neural Network to identify fake faces. Another study [19] used Haar-like features and Linear Discriminant Analysis to analyze faces in photos and detect spoo ng attacks using the cooccurrence of adjacent local binary patterns (CoALBP). The author [20] used Fourier spectra of both 2D and 3D images to discover textural differences.…”

Section: Related Workmentioning

confidence: 99%