Electric Network Frequency (ENF) acts as a fingerprint in multimedia forensics applications. In indoor environments, ENF variations affect the intensity of light sources connected to power mains. Accordingly, the light intensity variations captured by sensing devices can be exploited to estimate the ENF. A first optical sensing device based on a photodiode is developed for capturing ENF variations in indoor lighting environments. In addition, a device that captures the ENF directly from power mains is implemented. This device serves as a ground truth ENF collector. Video recordings captured by a camera are also employed to estimate the ENF. The camera serves as a second optical sensor. The factors affecting the ENF estimation are thoroughly studied. The maximum correlation coefficient between the ENF estimated by the two optical sensors and that estimated directly from power mains is used to measure the estimation accuracy. The paper's major contribution is in the disclosure of extensive experimental evidence on ENF estimation in scenes ranging from static ones capturing a white wall to non-static ones, including human activity.

翻译：电网频率（ENF）在多媒体取证应用中可作为指纹特征。在室内环境中，ENF变化会影响连接至电力主线的光源强度。因此，可通过传感设备捕获的光强变化来估计ENF。开发了基于光电二极管的首个光学传感装置，用于捕获室内照明环境中的ENF变化。此外还实现了一种直接从电力主线捕获ENF的装置，该装置作为ENF真实值采集器。同时采用摄像机录制的视频片段进行ENF估计，摄像机作为第二种光学传感器。对影响ENF估计的因素进行了深入研究。以两种光学传感器估计的ENF与直接通过电力主线估计的ENF之间的最大相关系数作为衡量估计精度的指标。本文主要贡献在于，从静态场景（仅拍摄白墙）到包含人体活动的非静态场景中，披露了关于ENF估计的大量实验证据。