Leveraging Customized Training Datasets for Authentication of Surveillance Camera Videos in Warehouse Environments

The authentication of surveillance camera videos is a critical task in ensuring the integrity and reliability of security systems, particularly in warehouse environments where diverse activities can unfold amidst varying environmental conditions. This paper presents a comprehensive approach to addressing the challenges associated with this task through the utilization of customized training datasets for use with machine learning techniques. Drawing from the principles of machine learning, our methodology focuses on the creation and utilization of well-structured training datasets to train models for authenticating surveillance footage. These datasets serve multiple purposes, including model learning, generalization to unseen data, performance evaluation, hyperparameter tuning, and benchmarking against alternative approaches. A key challenge in this endeavor lies in the acquisition of high-quality training data that accurately represents the complexities of daily warehouse activities while adhering to regulatory requirements such as Executive Order (E.O.) 14110, which emphasizes the lawful, secure, and privacy-conscious development and use of artificial intelligence. To address this challenge, we employ a combination of custom dataset creation and strategic filming in controlled environments such as waste management warehouses and outdoor loading docks. Our approach encompasses the filming of staged events to capture a diverse range of activities, including vehicle movements, package deliveries, and pedestrian traffic, under various weather and lighting conditions. Additionally, we curate testing datasets that encompass common facility activities and scenarios where threat actors may attempt to alter or obscure surveillance footage through video manipulation techniques. By leveraging custom training datasets and meticulous data collection methodologies, our research contributes to the development of robust machine learning models capable of authenticating surveillance camera videos with enhanced accuracy and reliability in warehouse environments. We anticipate that our findings will not only advance the field of video authentication but also inform future efforts aimed at safeguarding security systems against potential threats posed by video manipulation.