Theoretical Idea On Using GAN Discriminator To Detect Visual Data Generated Using GAN

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Gaurav Singh Bisht; Pratik Bhavsing Gavit; Arnav Ashish Godhamgaonkar; Poshiya Harshil Himatbhai; Prof. J. S. Pawar

Theoretical Idea On Using GAN Discriminator To Detect Visual Data Generated Using GAN

Authors: Gaurav Singh Bisht, Pratik Bhavsing Gavit, Arnav Ashish Godhamgaonkar, Poshiya Harshil Himatbhai, Prof. J. S. Pawar

Unique Paper ID: 169123
Volume: 11
Issue: 6
PageNo: 352-358

Keywords: Deep fake Detection Deep Fake Adversarial Networks Machine Learning Generative Adversarial Networks(GAN)

Abstract:
With the rise in digital content generation, deep fake images have become a growing concern, posing threats to privacy, security, and credibility. This paper introduces a study on deep fake image detection tool based on Generative Adversarial Networks (GAN), which aims to differentiate authentic images from those synthetically generated. By leveraging deep learning, specifically the discriminator of a GAN framework, the system identifies inconsistencies in deep fake images, providing reliable detection for use in various fields such as media verification, cybersecurity, and legal applications. Our system employs a generator-discriminator architecture, where the discriminator is trained to recognize fake images generated by the generator, improving its ability to spot telltale signs of deep fakes. Trained on extensive datasets of both real and fake images, this model is able to learn subtle differences and accurately flag synthetic content. The goal of this tool is to enhance the detection of manipulated images, aiding sectors that require image authenticity verification.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 352-358

Theoretical Idea On Using GAN Discriminator To Detect Visual Data Generated Using GAN

Available:https://ijirt.org/article?manuscript=169123

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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