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ZENODO
Article . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
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Deepfakes: The Evolution of Hyper realistic Media Manipulation

Authors: Dr.A.Shaji George; A.S.Hovan George;

Deepfakes: The Evolution of Hyper realistic Media Manipulation

Abstract

Deepfakes, synthetic media created using artificial intelligence and machine learning techniques, allow for the creation of highly realistic fake videos and audio recordings. As deepfake technology has rapidly advanced in recent years, the potential for its misuse in disinformation campaigns, fraud, and other forms of deception has grown exponentially. This paper explores the current state and trajectory of deepfake technology, emerging safeguards designed to detect deepfakes, and the critical role of education and skepticism in inoculating society against their harms. The paper begins by providing background on what deepfakes are and how they are created using generative adversarial networks. It highlights the rising prevalence of deepfakes and the risks they pose for manipulating opinions, swaying elections, committing financial fraud, and damaging reputations. As deepfake creation tools become more accessible, the number of deepfakes in existence is likely to grow at an accelerated pace. Current deepfake techniques still leave subtle clues that allow detection by the human eye, like jerky movements, lighting shifts, and poor lip syncing. However, rapid improvements in realism and ease of generation mean technological safeguards are essential. The paper discusses digital authentication techniques like blockchain, AI-powered deepfake detection algorithms that identify image artifacts, and intentional video watermarking to disrupt deepfake creation. While technology can help label deepfakes, human discernment remains critical. Through education on how to spot signs of manipulated media, applying skepticism instead of blindly trusting videos and audio, and following cybersecurity best practices, individuals can minimize risks. However, the arms race between deepfake creation and detection means no solution will be foolproof. To conclude, the paper argues that relying solely on technological safeguards is insufficient. Rather, a multipronged societal response combining technological defenses, widespread public awareness, and conscientious skepticism is required to meet the epochal challenge posed by the evolution of deepfakes and other forms of synthetic media. As deepfakes grow more sophisticated, we must dedicate resources toward understanding and exposing manipulated media in order to inoculate ourselves against potentially significant social harms.

Keywords

Deepfakes, Generative Adversarial Networks (GANs), Authentication, Detection, Awareness, Skepticism, Cybersecurity, Disinformation, Arms race, Resilience.

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Top 10%
Top 10%
Green