Poster: Camera Tampering Detection for Outdoor IoT Systems

TL;DR

This paper presents two methods for detecting tampering in outdoor IoT camera images, comparing their accuracy and resource needs, and provides datasets to aid future research.

Contribution

It introduces a rule-based and a deep-learning approach for camera tampering detection in still images and offers publicly available datasets for evaluation.

Findings

Deep-learning method achieves higher accuracy.

Rule-based method is more resource-efficient.

Datasets include normal, blurred, and rotated images.

Abstract

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.