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Research Paper | Computers & Electrical Engineering | United States of America | Volume 13 Issue 3, March 2024 | Rating: 5.6 / 10

Advancing Faux Image Detection: A Hybrid Approach Combining Deep Learning and Data Mining Techniques

Srinivas Naveen D. Surabhi ^[2] | Chirag Vinalbhai Shah | Vishwanadham Mandala ^[5] | Priyank Shah ^[2]

Abstract: In this study, we address the growing concern of faux images and videos in digital media, which pose significant risks in terms of misinformation and security. We introduce a novel hybrid detection framework that combines the strengths of deep learning and data mining to efficiently distinguish between authentic and manipulated content. By leveraging advanced feature extraction techniques and ensemble learning models, our approach demonstrates superior accuracy in identifying faux images across diverse datasets. The effectiveness of our method highlights the importance of innovative solutions in the battle against digital manipulation, offering a promising direction for future research in media authenticity and security.

Keywords: Faux Image Detection, Data Pre - Processing, Data visualization, Image Pre Processing, Classification Models

Edition: Volume 13 Issue 3, March 2024,

Pages: 959 - 963