Deepfake Detection Using Machine Learning Techniques: A Scalable Solution for Media Integrity

Pratik Godase; Sanket Pawar; Rohan Dhengale; Shavarsiddha Gurav; Avinash Kapare

doi:10.32628/CSEIT241061130

Authors

Pratik Godase UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India Author
Sanket Pawar UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India Author
Rohan Dhengale UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India Author
Shavarsiddha Gurav UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India Author
Avinash Kapare UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India Author

DOI:

https://doi.org/10.32628/CSEIT241061130

Keywords:

Deepfake Detection, Machine Learning, Media Integrity, Convolutional Neural Networks, Temporal Analysis

Abstract

The proliferation of deepfake technology has introduced unprecedented challenges to media integrity and public trust. Leveraging advanced machine learning techniques, this study proposes a scalable solution for detecting deepfake content in digital media. Our methodology employs convolutional neural networks (CNNs) to capture spatial inconsistencies and recurrent neural networks (RNNs) to analyze temporal patterns in video data, ensuring comprehensive detection capabilities. Utilizing publicly available deepfake datasets, the proposed framework demonstrates high accuracy and robustness against various forgery methods. The results highlight the model's potential to address real-world challenges, offering a reliable approach to identifying manipulated content across diverse platforms. This research not only contributes to the growing field of deepfake detection but also underscores the critical role of scalable and adaptable solutions in combating the misuse of AI-generated media. Future work will focus on enhancing model generalization across emerging deepfake generation techniques.

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