StegGAN: hiding image within image using conditional generative adversarial networks

Singh, Brijesh; Sharma, Prasen Kumar; Huddedar, Shashank Anil; Sur, Arijit; Mitra, Pinaki

doi:10.1007/s11042-022-13172-9

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…Remote semi-structured interviews with 70 law students from all grades who volunteered were transcribed and coded independently by two researchers and analyzed thematically using service learning and community of practice as sensitive concepts. The literature [21] proposes an architecture based on conditional generative adversarial networks using steganography and statistical academic agnosticism. The proposed approach ensures statistical undetectability of teaching quality by combining perceptual loss functions and adversarial training.…”

Section: Introductionmentioning

confidence: 99%

A digital teaching model for jurisprudence courses based on a linear regression model

Luo

2023

Applied Mathematics and Nonlinear Sciences

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To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.

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Section: Introductionmentioning

confidence: 99%