International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Deepfake detection using machine learning is essential in safeguarding digital content authenticity. This project utilizes CNNs for image analysis, SVMs for audio verification, and Bayesian models for video scrutiny. By refining detection techniques, it ensures reliable identification of manipulated media and enhances digital security against evolving threats. system begins with data preprocessing, removing noise and extracting features essential for analysis. Machine learning models are trained on diverse datasets containing both genuine and synthetic content. Advanced classification algorithms then determine manipulation likelihood, continuously adapting to increasingly sophisticated deepfake generation methods for improved accuracy, By integrating multiple AI techniques, this project provides an automated solution for identifying manipulated content across various multimedia formats. Strengthening digital trust, it addresses growing concerns over misinformation while contributing to ethical AI applications that preserve content integrity, privacy, and authenticity in modern digital communication. The rapid growth of artificial intelligence has made it easier to create convincing fake media, posing serious risks in areas like politics, entertainment, and social media. As fake content becomes more widespread, effective detection methods are crucial. Current approaches struggle to keep up with evolving deepfake technologies, creating a need for reliable solutions. This paper proposes using convolutional neural networks to analyse facial features and motion inconsistencies in videos, aiming to improve detection accuracy. Additionally, audio analysis will be integrated to detect mismatches between sound and visuals, enhancing the model’s effectiveness. The research emphasizes the importance of simple and effective methods to address the challenges of fake media.

Keywords: Deepfake detection, Machine learning, Digital content authenticity, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Image analysis, Video scrutiny, Data preprocessing, Feature extraction, Manipulation detection, Synthetic media, Classification algorithms, Adversarial threats, Multimedia forensics, Digital trust, Misinformation, Ethical AI, Content integrity, Privacy, Facial feature analysis, Motion inconsistencies, Audio‑visual mismatch, Deepfake generation methods, Automated detection system, Reliable detection solutions.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412117