Deep Convolution Neural Networks for Robust Live Detection of Object-based Forgeries in Video
Dama Rajeswari,
Makasi Sri Lakshmi,
Kottala Sravanthi,
Sanjamala Venkata Sai Vamsi,
Jana Vara Prasad
Video forgery detection is a critical aspect of digital forensics, addressing the challenges posed by
the manipulation of video content. This paper presents a novel approach for video forgery detection
using Deep Convolutional Neural Networks (DCNN). Leveraging the power of deep learning, our
method aims to improve the accuracy and efficiency of object-based forgery detection in advanced video
sequences. In the proposed approach, we build upon the foundation of an existing method, which utilizes
Convolutional Neural Networks, and introduce innovative modifications to the DCNN architecture.
These modifications include data preprocessing, network architecture, and training strategies that
enhance the model’s ability to detect tampered objects in video frames. We conduct experiments on the
SYSUOBJFORG dataset, the largest objectbased forged video dataset to date, with advanced video
encoding standards. Our DCNNbased approach is compared with the existing method, demonstrating
superior performance. The results show increased accuracy and robustness in detecting object-based
video forgery. This paper not only contributes to the field of video forgery detection but also underscores
the potential of sdeep learning, particularly DCNN, in addressing the evolving challenges of digital
video manipulation. The findings open avenues for future research in the localization of forged regions
and the application of DCNN in lower bitrate or lower resolution video sequences.
