Conference proceeding
A Video Camera Model Identification System Using Deep Learning and Fusion
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), v 2019-, pp 8271-8275
May 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
While significant work has been conducted to perform source camera model identification for images, little work has been done specifically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identification for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed. In this paper, we propose a deep learning based system for determining the source camera model that captured a digital video. To do this, we use a convolutional neural network to produce camera model identification scores for small patches taken from video frames. These patches are chosen by a patch selection system that obtains patches from several appropriate frames temporally distributed throughout the video. Forensic information obtained by the CNN is provided to a fusion system, which combines it to produce a single, more accurate identification result. Through a series of experiments, we evaluate several system design choices and show that our system can achieve 95.9% video camera model identification accuracy.
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Details
- Title
- A Video Camera Model Identification System Using Deep Learning and Fusion
- Creators
- B Hosler - Drexel UniversityO Mayer - Drexel UniversityB Bayar - Drexel UniversityX Zhao - Drexel UniversityC Chen - Drexel UniversityJ. A Shackleford - Drexel UniversityM. C Stamm - Drexel UniversityIEEE
- Publication Details
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), v 2019-, pp 8271-8275
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Medicine (Graduate)
- Web of Science ID
- WOS:000482554008102
- Scopus ID
- 2-s2.0-85068252104
- Other Identifier
- 991019170346604721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Acoustics
- Engineering, Electrical & Electronic