The digital resurrection of Margaret Thatcher: Creative, technological and legal dilemmas in the use of deepfakes in screen drama

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Abstract

This article develops from the findings of an interdisciplinary research project that has linked film practice research with computer science and law, in an exercise that seeks to digitally resurrect Margaret Thatcher to play herself in a contemporary film drama. The article highlights the imminent spread of machine learning techniques for digital face replacement across fiction content production, with central research questions concerning the ethical and legal issues that arise from the appropriation of the facial image of a deceased person for use in drama.

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Image Super-Resolution Using Deep Convolutional Networks.

Chao Dong, Chen Change Loy, Kaiming He … (2016)

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.