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Toward video tampering exposure: inferring compression parameters from pixels.

Johnston, Pamela; Elyan, Eyad; Jayne, Chrisina

Authors

Chrisina Jayne



Contributors

Elias Pimenidis
Editor

Chrisina Jayne
Editor

Abstract

Video tampering detection remains an open problem in the field of digital media forensics. Some existing methods focus on recompression detection because any changes made to the pixels of a video will require recompression of the complete stream. Recompression can be ascertained whenever there is a mismatch between compression parameters encoded in the syntax elements of the compressed bitstream and those derived from the pixels themselves. However, deriving compression parameters directly and solely from the pixels is not trivial. In this paper we propose a new method to estimate the H.264/AVC quantisation parameter (QP) in frame patches from raw pixels using Convolutional Neural Networks (CNN) and class composition. Extensive experiments show that QP of key-frames can be estimated using CNN. Results also show that accuracy drops for predicted frames. These results open new, interesting research directions in the domain of video tampering/forgery detection.

Citation

JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Toward video tampering exposure: inferring compression parameters from pixels. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 44-57, Available from: https://doi.org/10.1007/978-3-319-98204-5_4

Conference Name 19th International conference on engineering applications of neural networks (EANN 2018)
Conference Location Bristol, UK
Start Date Sep 3, 2018
End Date Sep 5, 2018
Acceptance Date May 31, 2018
Online Publication Date Jul 27, 2018
Publication Date Jul 27, 2018
Deposit Date Oct 15, 2018
Publicly Available Date Jul 28, 2019
Print ISSN 1865-0929
Electronic ISSN 1865-0937
Publisher Springer
Pages 44-57
Series Title Communications in computer and information science
Series Number 893
Series ISSN 1865-0937
ISBN 9783319982038
DOI https://doi.org/10.1007/978-3-319-98204-5_4
Keywords CNN; Compression; Video tampering detection
Public URL http://hdl.handle.net/10059/3170

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