Deepfake Technology Research Articles

LEGAL ASPECTS OF DEEPFAKES

The purpose of this article is to bring closer the legal aspects of deepfake. Beginning with what exactly deepfake is and what its history is, in what areas it is and can be used and what risks and benefits it brings. This article distinguishes between civil law and criminal law aspects. Furthermore, the authors have indicated examples of legal regulations of deepfake, which were adopted in such legal systems as the USA or China. At the end of the article, the authors give the potential evolutionary paths of legal solutions concerning deepfake technology.

Beyond Porn and Discreditation: Epistemic Promises and Perils of Deepfake Technology in Digital Lifeworlds

AbstractDeepfakes are a new form of synthetic media that broke upon the world in 2017. Bringing photoshopping to video, deepfakes replace people in existing videos with someone else’s likeness. Currently most of their reach is limited to pornography, and they are also used to discredit people. However, deepfake technology has many epistemic promises and perils, which concern how we fare as knowers. Our goal is to help set an agenda around these matters, to make sure this technology can help realize epistemic rights and epistemic justice and unleash human creativity, rather than inflict epistemic wrongs of any sort. Our project is exploratory in nature, and we do not aim to offer conclusive answers at this early stage. There is a need to remain vigilant to make sure the downsides do not outweigh the upsides, and that will be a tall order.

Microfake: How small-scale deepfakes can undermine society

Advances in deepfake technology have led to the emergence of a new picture of how doctored material will be used in disinformation campaigns. While safeguards ensure that manipulated videos may not be such a problem at the highest levels of security and defence, lower levels – such as local elections – remain vulnerable to malign actors. At such levels, deepfakes can be distributed using social media channels to target unsuspecting victims. Current solutions only protect individuals who are prominent enough to be covered by the mainstream media, and not enough is being done by governments or social media companies to protect ordinary users from coordinated inauthentic activity online. However, with more images and videos of ourselves online than ever before, anyone can be a victim of a disinformation campaign. As deepfakes become easier to make, no one is safe – hyper-localized manipulation will create problems for democratic institutions that have not yet been fully understood.

The Deepfake Challenges and Deepfake Video Detection

Deepfake is a combination of fake and deep-learning technology. Deep learning is the function of artificial intelligence that can be used to create and detect deepfakes. Deepfakes are created using generative adversarial networks, in which two machine learning models exit. One model trains on a dataset and then creates the deepfakes, and the other model tries to detect the deepfakes. The forger creates deepfakes until the other model can't detect the deepfakes. Deepfakes creating fake videos, images, news, and terrorist events. When deepfake videos, and images increase on social media people will ignore to trust the truth. Deepfakes are increasingly affecting individuals, communities, organizations, security, religions, and democracy. This paper aims to investigate deepfake challenges, and to detect deepfake videos by using eye blinking. Deepfake detections are methods to detect real or deepfake images and videos on social media. Deepfake detection techniques are needed original and fake images or video datasets to train the detection models. In this study, first discussed deepfake technology and its challenges, then identified available video datasets. Following, convolutional neural networks to classify the eye states and long short term memory for sequence learning has been used. Furthermore, the eye aspect ratio was used to calculate the height and width of open and closed eyes and to detect the blinking intervals. The model trained on UADFV dataset to detect fake and real video by using eye blinking and detects 18.4 eye blinks per minute on the real videos and 4.28 eye blinks per minute on fake videos. The overall detection accuracy on real and fake videos was 93.23% and 98.30% respectively. In the future research and development needs more scalable, accurate, reliable and cross-platform deepfake detection techniques.

Situational Awareness of Deepfake Technology

This work is a situational awareness reporting of the deepfake technology, which is a technology where multimedia components such as video. Image or audio of an individual is distorted with the aim of disinforming or misinforming the content viewers. The technology first appeared in November, 2017, when a social media user posted an algorithm that used AI to create fake videos. This paper will discuss areas where deepfake has been applied, some researches of how to create deepfake, and also some researches on how to detect deepfake. The study is mainly to report this new technology thereby making the society aware of its existence and watching out for its use. Keywords: Deepfakes, Awareness, Security, Social Media, Online, Images, Videos, Users, Technology Aims Research Journal Reference Format: Olebara, C.C. (2020): Situational Awareness of Deepfake Technology”. Advances in Multidisciplinary Research Journal. Vol. 6, Pp 153-157. Article DOI: dx.doi.org/10.22624/AIMS/V6N1P14. Available online at www.isteams.net/aimsjournal.

Nothing new here: Emphasizing the social and cultural context of deepfakes

In the last year and a half, deepfakes have garnered a lot of attention as the newest form of digital manipulation. While not problematic in and of itself, deepfake technology exists in a social environment rife with cybermisogyny, toxic-technocultures, and attitudes that devalue, objectify, and use women’s bodies against them. The basic technology, which in fact embodies none of these characteristics, is deployed within this harmful environment to produce problematic outcomes, such as the creation of fake and non-consensual pornography. The sophisticated technology and metaphysical nature of deepfakes as both real and not real (the body of one person, the face of another) makes them impervious to many technical, legal, and regulatory solutions. For these same reasons, defining the harm deepfakes causes to those targeted is similarly difficult and very often targets of deepfakes are not afforded the protection they require. We argue that it is important to put an emphasis on the social and cultural attitudes that underscore the nefarious use of deepfakes and thus to adopt a more material-based approach, opposed to technological, to understanding the harm presented by deepfakes.

Are Deep Fakes a Shallow Concern? A Critical Analysis of the Likely Societal Reaction to Deep Fakes

Deep fakes, a class of AI generated audio-visual materials designed to appear as an authentic record of actual speech, garnered increasing attention as worries of foreign disinformation campaigns and so called “fake news” have increased. Some critics raise concerns that this new technology will be too realistic to differentiate fact from fiction, allowing bad actors to manipulate elections, induce societal unrest, and incite panic. In this view, the influx of deep fake content may lead to the death of trust in media outright, as people will assume all content may be artificially-generated “fake news.” Yet close consideration of the hypotheses put forth so far reveals an unstated assumption that has not yet received attention: that deep fakes, once they are technologically advanced and easy to produce, will either be believed without question or will fundamentally shift public perceptions of video such that even real ones will be dismissed. This paper aims to fill the gap in the literature by critiquing the assumption that deep fakes, in the end, will necessarily fool the public into believing lies or rejecting truth. In fact, the likely societal reaction outcome may lie somewhere in the middle, in which society develops proxy mechanisms for assessing the reliability of video evidence in the wake of deep fake technology. That likelihood is based on two classes of observations. First, the reason we trust images and video may stem largely from societal norms about the use of the medium, rather something inherent to the medium itself. Second, history has shown that similar concerns about digital photo editing techniques did not lead to either of the outcomes predicted for societal perceptions of truth; how society reacted to fake photos sheds much light on what is likely to happen with regard to deep fake videos. Identifying the likely and less-drastic social trends in reaction to deep fakes is exceptionally important today, because ongoing fears of the technology have prompted calls for regulatory responses. For example, many propose amending Section 230 of the Communications Decency Act (“CDA 230”) to increase liability on platforms who do not take reasonable steps to limit the spread of deep fake content on their platforms. To the extent that there is a likely scenario in which ordinary operations of society are likely to manage the impact of deep fakes on perceptions of truth, the need for policy responses (that no doubt will be imperfect and potentially detrimental to valuable technological advances) is strongly lessened. This paper proceeds by covering two main areas: emerging technologies and online platform regulation. It first explains the likely reaction to deep fakes by reviewing the development of similar technologies as well as the key distinctions from technologies of the past. Second, the paper examines whether regulatory responses to deep fakes, focusing primarily on calls to amend CDA 230, are necessary or whether existing regulatory tools and free market forces will be sufficient.

The Emergence of Deepfake Technology: A Review

The Emergence of Deepfake Technology: A Review

Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security

Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating the technology’s sophistication, making deep fakes ever more realistic and increasingly resistant to detection. Deep-fake technology has characteristics that enable rapid and widespread diffusion, putting it into the hands of both sophisticated and unsophisticated actors. While deep-fake technology will bring with it certain benefits, it also will introduce many harms. The marketplace of ideas already suffers from truth decay as our networked information environment interacts in toxic ways with our cognitive biases. Deep fakes will exacerbate this problem significantly. Individuals and businesses will face novel forms of exploitation, intimidation, and personal sabotage. The risks to our democracy and to national security are profound as well. Our aim is to provide the first in-depth assessment of the causes and consequences of this disruptive technological change, and to explore the existing and potential tools for responding to it. We survey a broad array of responses, including: the role of technological solutions; criminal penalties, civil liability, and regulatory action; military and covert-action responses; economic sanctions; and market developments. We cover the waterfront from immunities to immutable authentication trails, offering recommendations to improve law and policy and anticipating the pitfalls embedded in various solutions.