Poster: Camera Tampering Detection for Outdoor IoT Systems
This addresses camera tampering detection for outdoor surveillance systems, but it is incremental as it builds on existing methods for a specific challenge.
The study tackled the problem of detecting camera tampering in outdoor IoT systems using still images, proposing rule-based and deep-learning methods, with results showing the deep-learning model achieves higher accuracy while the rule-based method is more suitable for resource-limited scenarios.
Recently, the use of smart cameras in outdoor settings has grown to improve surveillance and security. Nonetheless, these systems are susceptible to tampering, whether from deliberate vandalism or harsh environmental conditions, which can undermine their monitoring effectiveness. In this context, detecting camera tampering is more challenging when a camera is capturing still images rather than video as there is no sequence of continuous frames over time. In this study, we propose two approaches for detecting tampered images: a rule-based method and a deep-learning-based method. The aim is to evaluate how each method performs in terms of accuracy, computational demands, and the data required for training when applied to real-world scenarios. Our results show that the deep-learning model provides higher accuracy, while the rule-based method is more appropriate for scenarios where resources are limited and a prolonged calibration phase is impractical. We also offer publicly available datasets with normal, blurred, and rotated images to support the development and evaluation of camera tampering detection methods, addressing the need for such resources.