AI- Based Cybersecurity Policies and Procedures
K Bilwatej Kumar,
M Jayasudhs,
D Hajeepeera,
K Dattasai,
V Navitha,
S Muzambil
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 speed
This form of tampering, known as object-based forgery, poses a particular challenge for detection.
Traditional methods 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.
