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.

翻译：近年来，智能摄像头在户外环境中的应用日益广泛，以提升监控与安防能力。然而，这些系统易受篡改影响，无论是蓄意破坏还是恶劣环境条件，均可能削弱其监控效能。在此背景下，当摄像头采集静态图像而非视频时，由于缺乏随时间连续的帧序列，检测摄像头篡改更具挑战性。本研究提出两种检测篡改图像的方法：基于规则的方法与基于深度学习的方法。目标在于评估每种方法在应用于实际场景时，在准确性、计算需求及训练所需数据方面的表现。我们的结果表明，深度学习模型具有更高的准确性，而基于规则的方法更适用于资源有限且无法进行长期校准阶段的场景。我们还公开提供了包含正常、模糊及旋转图像的数据集，以支持摄像头篡改检测方法的开发与评估，满足对此类资源的需求。