Smart Tamper Detection: A Machine Learning Approach for Security and Threats Purposes

In an era defined by digital media, the authenticity of video content is paramount. The proliferation of sophisticated video editing tools such as Adobe Photoshop has made tampering increasingly accessible, posing significant threats to security, trust, and justice. This paper presents a Video Tampering Detection System that leverages the power of machine learning to differentiate between genuine and tampered videos. The system, implemented using Python 3.12, utilizes libraries such as scikit-learn, TensorFlow, and OpenCV for efficient frame extraction and analysis. The proposed approach employs a Jupyter notebook environment to develop and manage the machine learning pipeline, categorizing functionalities into “data,” “model,” “sample,” and “videos.” This modular design facilitates efficient code organization, execution, and scalability. The system aims to provide users with a reliable tool to verify the integrity of video content. By analyzing extracted frames, the system assigns a confidence score ranging from 0 to 1, indicating the likelihood of tampering. A score closer to 0 suggests authenticity, while a score approaching 1 indicates a higher probability of manipulation. The data from 58 videos had been trained and the prediction results were analysed. The results show that the prediction results reached 95% accuracy when the videos trained were 58, while the accuracy was only 78% when the trained videos were 28. It shows that the more videos trained, the more accurate the prediction is. This research contributes to the ongoing efforts in multimedia forensics by providing a robust and user-friendly system for video tampering detection.