Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification

Abstract

There is a high demand for multimedia forensics analysts to locate the original camera of photographs and videos that are being taken nowadays. There has been considerable progress in the technology of identifying the source of data, which has enabled conflict resolutions involving copyright infringements and identifying those responsible for serious offences to be resolved. This study focuses on the issue of identifying the camera model used to acquire video sequences used in this research that is, identifying the type of camera used to capture the video sequence under investigation. For this purpose, we created two distinct CNN-based camera model recognition techniques to be used in an innovative multi-modal setting. The proposed multi-modal methods combine audio and visual information in order to address the identification issue, which is superior to mono-modal methods which use only the visual or audio information from the investigated video to provide the identification information.According to legal standards of admissible evidence and criminal procedure, Forensic Science involves the application of science to the legal aspects of criminal and civil law, primarily during criminal investigations, in line with the standards of admissible evidence and criminal procedure in the law. It is responsible for collecting, preserving, and analyzing scientific evidence in the course of an investigation. It has become a critical part of criminology as a result of the rapid rise in crime rates over the last few decades. Our proposed methods were tested on a well-known dataset known as the Vision dataset, which contains about 2000 video sequences gathered from various devices of varying types. It is conducted experiments on social media platforms such as YouTube and WhatsApp as well as native videos directly obtained from their acquisition devices by the means of their acquisition devices. According to the results of the study, the multimodal approaches suggest that they greatly outperform their mono-modal equivalents in addressing the challenge at hand, constituting an effective approach to address the challenge and offering the possibility of even more difficult circumstances in the future