MMGANGuard: A Robust Approach for Detecting Fake Images Generated by GANs Using Multi-Model Techniques

Recent advances in Generative Adversarial Networks (GANs) have produced synthetic images with high visual fidelity, making them nearly indistinguishable from human-created images. These synthetic images referred to as deepfakes, have become a major source of misinformation due to social media. Techn...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 104153 - 104164
Main Authors:	Ali Raza, Syed, Habib, Usman, Usman, Muhammad, Ashraf Cheema, Adeel, Sajid Khan, Muhammad
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Computer architecture Data analysis data analytics Deception Deep fake Deep learning Deepfake Deepfakes detection Digital imaging Error analysis Error detection Forensic sciences GANs Generative adversarial networks Image coding Image manipulation multi-model Neurons Predictive models Social networking (online) StyleGAN Synthetic data
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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