A Comprehensive Framework for Forged Smartphone Video Detection with Dataset Development and Spatial Temporal Analysis

DOI

10.2991/978-94-6463-718-2_152How to use a DOI?

Keywords

video forgery detection; smartphone videos; dataset development; spatial-temporal analysis; deep learning; digital forensics; deepfake detection

Abstract

With the growing prevalence of counterfeit smartphone videos during the digital content boom, verifying the authenticity of the content has become more of a challenge. We present a new framework for forged smartphone video detection, featuring novel spatial-temporal analytics and a custom dataset. This framework takes advantage of convolutional and recurrent neural networks to learn spatial inconsistencies, as well as temporal anomalies, allowing powerful detection capable of identifying splicing, duplication, and deepfake attacks. Real world scenarios were used to deviate from common use cases of such a device, resulting in a new dataset that was developed from multiple device and tampering methods that highlight the characteristics of the use cases to ensure it will be applicable to real world cases. The approach achieves high accuracy and scalability and promises to be useful in entities such as journalism, digital forensics, and content moderation. The effectiveness of the proposed framework is verified by experimental results, demonstrating a substantial improvement over the state-of-the-art approaches. It serves as a reference point for further developments in the field of video forgery detection and provides a useful weapon in the fight for the verification of smartphone videos.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Rajivkannan
AU  - M. Venkatesan
AU  - V. Sharmila
AU  - G. Mukesh
AU  - J. Sridhar
AU  - J. Sujendran
PY  - 2025
DA  - 2025/05/23
TI  - A Comprehensive Framework for Forged Smartphone Video Detection with Dataset Development and Spatial Temporal Analysis
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1841
EP  - 1856
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_152
DO  - 10.2991/978-94-6463-718-2_152
ID  - Rajivkannan2025
ER  -