An Intelligent AI–ML Framework for Deepfake Generation and Detection

Year : 2026 | Volume : 13 | Issue : 02 | Page : 25 32
By

P.B. Kudal,

D.S. Joshi,

  1. HoD, Department of Artificial Intelligence and Machine Learning, Guru Gobind Singh Polytechnic, Maharashtra, India
  2. Lecturer, Department of Artificial Intelligence and Machine Learning, Guru Gobind Singh Polytechnic, Maharashtra, India

Abstract

Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have led to the rise of deepfake technology, making it one of the most impactful innovations in the digital world. Deepfakes are synthetic forms of digital media generated using deep learning algorithms capable of realistically manipulating facial expressions, voice patterns, images, and video content. Although these technologies provide innovative applications in entertainment, virtual reality, healthcare, and education, their misuse has created serious concerns related to misinformation, cybercrime, privacy violations, identity fraud, and political manipulation. The increasing realism of deepfake content has made manual identification extremely difficult, thereby creating a strong need for reliable automated detection systems. This research paper presents an AI–ML based deepfake detection framework designed to identify manipulated digital media using advanced deep learning techniques. The proposed system incorporates preprocessing, feature extraction, model training, and intelligent classification mechanisms to analyze visual inconsistencies commonly introduced during deepfake generation. Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) are utilized to detect abnormalities such as irregular facial textures, inconsistent lighting conditions, unnatural eye movements, and lip synchronization mismatches. The framework is trained using benchmark datasets containing both authentic and manipulated samples to improve detection accuracy and robustness. The paper also discusses the overall system architecture, detection methodology, algorithms, applications, challenges, and future research opportunities associated with deepfake technologies. The proposed approach contributes toward strengthening cybersecurity and digital forensics by providing an efficient mechanism for detecting forged media. The study highlights the importance of adaptive AI-driven detection systems in preserving digital authenticity, maintaining public trust, and minimizing the harmful effects of synthetic misinformation in modern digital communication environments.

Keywords: Artificial intelligence, machine learning, deepfake Detection, CNN, DNN, digital forensics, cyber security

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article: P.B. Kudal, D.S. Joshi. An Intelligent AI–ML Framework for Deepfake Generation and Detection. Journal of Artificial Intelligence Research & Advances. 2026; 13(02):25-32.
How to cite this URL: P.B. Kudal, D.S. Joshi. An Intelligent AI–ML Framework for Deepfake Generation and Detection. Journal of Artificial Intelligence Research & Advances. 2026; 13(02):25-32. Available from: https://journals.stmjournals.com/joaira/article=2026/view=260211

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Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 05/05/2026
Accepted 02/07/2026
Published 06/07/2026
Publication Time 62 Days

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