Research Article
Double JPEG Compression Detection Based on Fusion Features
@INPROCEEDINGS{10.1007/978-3-319-73564-1_16, author={Fulong Yang and Yabin Li and Kun Chong and Bo Wang}, title={Double JPEG Compression Detection Based on Fusion Features}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Double compression detection DCT coefficients Likelihood probability ratio features Benford features}, doi={10.1007/978-3-319-73564-1_16} }- Fulong Yang
Yabin Li
Kun Chong
Bo Wang
Year: 2018
Double JPEG Compression Detection Based on Fusion Features
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_16
Abstract
Detection of double JPEG compression plays an increasingly important role in image forensics. This paper mainly focuses on the situation where the images are aligned double JPEG compressed with two different quantization tables. We propose a new detection method based on the fusion features of Benford features and likelihood probability ratio features in this paper. We believe that with the help of likelihood probability ratio features, our fusion features can expose more artifacts left by double JPEG compression, which lead to a better performance. Comparative experiments have been carried out in our paper, and experimental result shows our method outperforms the baseline methods, even when one of the quality factors is pretty high.
