Double JPEG Compression Detection Based on Markov Model

Document Type

Conference Proceeding

Publication Date

1-1-2020

Abstract

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case.

Identifier

85083725115 (Scopus)

ISBN

[9783030435745]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-43575-2_11

e-ISSN

16113349

ISSN

03029743

First Page

141

Last Page

149

Volume

12022 LNCS

Grant

61502241

Fund Ref

National Natural Science Foundation of China

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