Poster: Camera Tampering Detection for Outdoor IoT Systems

2026-02-05

AI Summary

This study addresses the vulnerability of outdoor IoT cameras operating in static image mode to physical tampering and environmental interference, for which effective detection mechanisms are currently lacking. The authors propose two complementary approaches—one based on conventional image rules and the other leveraging deep learning—and systematically evaluate their accuracy, computational overhead, and data requirements in real-world scenarios. As the first work to offer a comprehensive comparison of these paradigms for static-image tamper detection, the study also introduces a publicly available dataset comprising normal, blurred, and rotated images. Experimental results demonstrate that while deep learning achieves higher detection accuracy, rule-based methods are better suited for resource-constrained deployments where continuous calibration is impractical.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• camera tampering
• outdoor IoT systems
• static image surveillance
• tampering detection

Innovation

• camera tampering detection
• rule-based method
• deep learning
• static image analysis
• IoT security