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A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION

Citation Author(s):
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm
Submitted by:
Brian Hosler
Last updated:
27 March 2019 - 9:03am
Document Type:
Research Manuscript
Document Year:
2019
Event:
Presenters Name:
Brian Hosler
Paper Code:
3577
Categories:

Abstract 

Abstract: 

While significant work has been conducted to perform source cam- era model identification for images, little work has been done specif- ically 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 identifi- cation 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 iden- tification 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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ICASSP2019.pdf

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