Privacy and Other Legal Concerns in the Wake of Deepfake Technology: Comparative Study of India, US, and China

Privacy and Other Legal Concerns in the Wake of Deepfake Technology: Comparative Study of India, US, and China

Purva Kaushik (Amity Law School, Guru Gobind Singh Indraprastha University, Delhi, India)
Copyright: © 2022 |Pages: 13
DOI: 10.4018/978-1-7998-8641-9.ch003
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The world is filled with technology-driven systems and instruments in the present era. Artificial intelligence (hereinafter referred to as “AI”) is increasingly dominating our everyday life. Deepfakes is one such AI-based technological innovation that is leading to huge concerns amongst legal and scientific experts. There have been recent instances wherein videos of statements never made and acts never done by public figures became viral on the internet. Face swap in videos is easily done for the purpose of cyber bullying and harassment of innocent victims. Since creation of these synthetic contents called deepfakes is becoming easier by the day, the growing concerns surrounding them demand immediate attention. This chapter shall specifically probe into the deepfake technology with dual objectives (i.e., to understand the causes of concern surrounding deepfakes related AI-technology and to compare effective ideas and regulatory measures of nations abroad such as US and China) in order to arrive at possible solutions to appropriately tackle this issue in India.
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The goal of this chapter is to understand the potential risks involved in the use of deepfake technology. There is an old saying - “Seeing is believing”. However, in the wake of the advancing deepfake technology, it could be dangerous for individuals as well as society to believe in what they view. Considering the fact that detection and regulatory measures concerning deepfakes are still in their nascent stage throughout the world, it is pertinent to be well aware of our technological and legal capabilities in the fight against this hazard. For the same purpose, the laws and regulatory frameworks of US and China shall also be analysed since these nations have proactively taken techno-legal measures to curb the havoc that deepfakes are capable of creating. Based on our critical understanding of these measures, we would be better equipped to look for solutions in the Indian set-up.

In order to reach solutions, we need to first understand the problem at hand. It all starts with the increasing involvement of social media platforms in the preceding decade. As a result, uploading of digital images and videos is trending worldwide. This has further led to creation of a significant number of new image/video-altering techniques and apps. In the given backdrop, deepfake origination took place.

Deepfakes have a close nexus with Digital Image Forensics Research Field. The term “deepfakes” is derived from a deep understanding of the technology that is required for creation of very realistic fake videos and images. Original faces, expressions and even voices are mimicked, altered and replaced by fake ones, thereby breaching social trust and creating privacy and other legal concerns. A deepfake is a product of AI and advanced machine learning algorithm called deep learning. It utilizes Generative Adversarial Networks (hereinafter referred to as “GAN”) for morphing original content. Simply understood, the role of GAN is to train two neural net architectures i.e., a generator (decoder) and a discriminator in an adversarial relationship (Smith & Mansted, 2020). Once the encoder has extracted the latent features of original face images, the generator (decoder) reconstructs the images and the discriminator detects whether or not the image created by generator is extremely real looking.

In this way, the generator-discriminator paired algorithms constantly compete against each other and constantly evolve. As a result, any defects in a deepfake can be instantly detected and corrected, thereby making this technology difficult to combat. Recently, multi-task Convolutional Neural Networks (hereinafter referred to as “CNN”) have been developed to increase the stability of face detection and reliability of face alignment in deepfakes. It involves image mapping wherein initially the algorithms used to work on massive data sets to extract, train and create content (Gerstner, 2020). However, with the constant advancements in this technology, now even a single image can be used to render a high quality deepfake which is difficult to detect by humans as well as machines. Another characteristic of this technology is that it requires very little skill and renders quick results in the form of extremely deceptive fake content.

Morphed images and videos are not new to this world, but extremely real looking ones are a recent development. It wasn’t until 2017, when the first public example of deepfakes surfaced that experts from the technological and legal field started viewing them as a threat. In this case, the faces of Hollywood celebrities were extracted from original images and superimposed onto faces of pornographic actresses by some anonymous users of the online platform of Reddit. Apart from that, certain non-pornographic deepfakes were also created by swapping the face of Nicholas Cage into numerous movie videos, though this was purely meant for entertainment purposes. This led to widespread circulation of deepfakes, thereby turning it into a new trend. In 2018, apps such as DeepNude and FakeApp surfaced which provided both free and paid platform to users for creating deepfakes and thereby harassing, blackmailing, bullying targets and spreading fake news (Schick, 2020). In the past 4 years, multiple apps have been created and made easily available to the public at large for creating deepfakes.

Deepfakes can be broadly divided into four categories based on the method of creation employed, namely – (1) Image deepfake which employs the method of face and body swapping;

Key Terms in this Chapter

Disinformation: A subset of misinformation, this is false information that is created and circulated with the malicious intention to deceive/ mislead the audience.

Morphed Image/Video: Changing an original image or video into another image or video with the assistance of seamless transition using computer-based techniques,

Generative Adversarial Networks (GAN): An advanced form of AI used for the creation of deepfakes. It involves the use of two neural networks (generator and discriminator) on previous data to compete against each other, thereby improving upon the output of each other and creating real looking morphed images and videos.

Artificial Intelligence (AI): The programming of machines in such a manner which allows them to study human intelligence through absorption of huge amounts of data and copy it in their actions.

Misinformation: False information that is created and circulated, with or without a malicious intention to deceive/ mislead the audience.

Silent Witness Theory: A rule of the law of evidence which allows acceptance of photo evidence even without verification by eye witnesses, if such evidence is produced through a reliable process which assures accuracy.

Deepfakes: Deceptive fake digital content created using AI and deep learning, which allows manipulation of original content. It may be for a positive or negative use.

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