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Browsing by Subject "Generative Adversarial Networks"

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    Combatting Deep-fakes in India – An Analysis of the Evolving Legal Paradigm and Its Challenges
    (University of North Bengal, 2024-03) Sarkar, Diya; De Sarkar, Sudipta
    Advancement of artificial intelligence and machine learning have spurred a new wave of propagation of false content and information about events and people, as part of entertainment, disparagement, fraud, influencing patterns and perceptions of consumers and voters etc., using deep-fake materials and targeted disinformation campaigns. Many institutions now perceive deep-fakes as a significantly greater hazard than identity theft, which can also be done with deep-fakes. This is especially true since the COVID-19 pandemic when most interactions went online. The advancement of deep-fake technology has reached a stage where the validity and integrity of any digital audio or video content available online may be called into question. This study presents a conceptualization of deep-fakes, explores their socio-legal ramifications, and evaluates the current legal ecosystem in the United States, Europe, and India. The authors through a comparative review intend to present constructive recommendations for addressing the difficulties posed by deep-fake technology and restoring trust within the digital ecosystem. The primary aim of the authors is to draw attention to the existing vulnerabilities linked to deep-fake technology and underscore the significance of implementing legislative regulations to effectively tackle these problems.
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