Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification (Informatica, 2023)
There is a high demand for multimedia forensics analysts to locate the originalcamera of photographs and videos that are being taken nowadays. There has been considerableprogress in the technology of identifying the source of data, which has enabled conflict resolutionsinvolving 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 sequencesused in this research that is, identifying the type of camera used to capture the video sequenceunder investigation. For this purpose, we created two distinct CNN-based camera model recognitiontechniques to be used in an innovative multi-modal setting. The proposed multi-modal methodscombine audio and visual information in order to address the identification issue, which is superiorto mono-modal methods which use only the visual or audio information from the investigated videoto provide the identification information.According to legal standards of admissible evidence andcriminal procedure, Forensic Science involves the application of science to the legal aspects of criminaland civil law, primarily during criminal investigations, in line with the standards of admissibleevidence and criminal procedure in the law. It is responsible for collecting, preserving, and analyzingscientific evidence in the course of an investigation. It has become a critical part of criminology as aresult of the rapid rise in crime rates over the last few decades. Our proposed methods were testedon a well-known dataset known as the Vision dataset, which contains about 2000 video sequencesgathered from various devices of varying types. It is conducted experiments on social media platformssuch as YouTube and WhatsApp as well as native videos directly obtained from their acquisitiondevices by the means of their acquisition devices. According to the results of the study, the multimodalapproaches suggest that they greatly outperform their mono-modal equivalents in addressingthe challenge at hand, constituting an effective approach to address the challenge and offering thepossibility of even more difficult circumstances in the future