NeighborWatch: Collaborative Detection of Malicious Devices in Overlapping Camera Networks

Abstract

Overlapping Camera Networks (OCNs) play a critical role in applications such as urban surveillance and public safety. However, their distributed architecture exposes them to threats from compromised cameras capable of submitting forged images. In this paper, we present NeighborWatch, the first unified framework to detect malicious nodes in OCNs by leveraging the inherent redundancy provided by overlapping fields of view (OFoV). Our approach comprises three complementary modules: Local Neighborhood Detection (LND), which verifies geometric and photometric consistency across neighboring views; Network-Level Detection (NLD), which propagates iterative trust credit to detect colluding clusters; and Latent Feature Consistency (LFC), which examines semantic coherence between OFoV and non-OFoV regions within each image. Extensive evaluations on real and simulated datasets demonstrate that NeighborWatch outperforms nine state-of-the-art image forgery detection techniques, achieving high detection accuracy and robust performance across diverse attack strategies and forgery types while requiring only minimal one-time training.