Volume - 4 | Issue - 2 | december 2025
@article{n.2025,
author = {Deepa N. and Edwin Joel P. and Cletus Sylphia P. and Ahalya G.},
title = {{Deep Sight: Unveiling Digital Deception}},
journal = {Recent Research Reviews Journal},
volume = {4},
number = {2},
pages = {325-337},
year = {2025},
publisher = {IRO Journals},
doi = {10.36548/rrrj.2025.2.008},
url = {https://doi.org/10.36548/rrrj.2025.2.008}
}
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Published
05 February, 2026
Deepfakes are rapidly increasing and generating severe problems with fake news, fraud and criminal behavior. The issue with AI-generated media has evolved into more accurate and recognizing fake data. The proposed "Deep Sight: Unveiling Digital Deception" is an AI/ML-driven system detects deepfake in various kinds of media including images, videos and audio. It compares differences in facial features, voice structures and action behavior to recognize fake data. This system separates each component (video, audio, images and contents) into real and fake data to improve transparency. It provides subtitle to show the sections of the text to be edited by AI. A built-in monitoring tool allows user to report possible data directly transferred to cybersecurity authorities for immediate action. The "Deep Sight" improves digital responsibility using advanced detection techniques and addressing AI-driven fraud. It enables people and organizations to protect media quality by avoiding modification. This concept proposes to create a safe global network by solving the increasing risk of deepfake technologies.
KeywordsDeepfakes AI Detection Media Forensics Cybersecurity Digital Integrity
