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Combating Deepfake-Generated Photos and Videos Using Generative Adversarial Network
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Author(s): B. Aarthi (SRM Institute of Science and Technology, Ramapuram, India), A. Smruthi (SRM Institute of Science and Technology, Ramapuram, India), Pamireddy Thanishka (SRM Institute of Science and Technology, Ramapuram, India), G. Sakthi Prasanna (SRM Institute of Science and Technology, Ramapuram, India)and P. Mahendran (Dhaanish Ahmed College of Engineering, India)
Copyright: 2026
Pages: 18
Source title:
Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-4672-4.ch003
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Abstract
Rapid advances in artificial intelligence and machine learning have resulted in the creation of Deep Fakes, which are manipulated films, audio, and images capable of disseminating false information, fake news, and altering sensitive records. The prevalence of deepfake technology has raised significant concerns regarding the veracity of digital content, underscoring the critical need for reliable deepfake facial recognition algorithms. This study embarks on developing an advanced deepfake detection system leveraging Generative Adversarial Networks (GANs) within a programming environment. The central focus is to create a neural network that can effectively differentiate between authentic and artificially generated media content. To accomplish this, the system undergoes extensive training using diverse datasets, enabling it to recognize subtle nuances and specific artifacts associated with GAN-generated content.
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