Research Article
Modelling GOP Structure Effects on ENF-Based Video Forensics
@INPROCEEDINGS{10.1007/978-3-030-68734-2_7, author={Pasquale Ferrara and Gerard Draper-Gil and Ignacio Sanchez and Henrik Junklewitz and Laurent Beslay}, title={Modelling GOP Structure Effects on ENF-Based Video Forensics}, proceedings={Digital Forensics and Cyber Crime. 11th EAI International Conference, ICDF2C 2020, Boston, MA, USA, October 15-16, 2020, Proceedings}, proceedings_a={ICDF2C}, year={2021}, month={2}, keywords={Electric network frequency Video Compression GOP Signal processing Forensics}, doi={10.1007/978-3-030-68734-2_7} }- Pasquale Ferrara
Gerard Draper-Gil
Ignacio Sanchez
Henrik Junklewitz
Laurent Beslay
Year: 2021
Modelling GOP Structure Effects on ENF-Based Video Forensics
ICDF2C
Springer
DOI: 10.1007/978-3-030-68734-2_7
Abstract
Electricity is transported through the network as alternate current, usually at a carrier frequency (50/60 Hz) which is known as Electric Network Frequency (ENF). In practice, ENF fluctuates around the nominal value because of changes in the supply and demand of power over time. These fluctuations are conveyed by the light that is emitted by sources connected to the power grid. Captured by video recordings, such localized variations can be exploited as digital watermarks in order to determine the position of a video in time (e.g. timestamping) and space, as well as to verify its integrity. However, the encoded formats of acquired videos alter the shape of ENF extracted from video frames. This paper provides an analytical model for characterizing the effects of group of pictures (GOP) structure adopted by the most widespread video encoders. The model is assessed through an experimental evaluation campaign, by analyzing different working conditions and by showing how the information from the GOP can contribute to the extraction of ENF from video frames.
