Detection of Double JPEG Compression with the Same Quantization Matrix Based on Convolutional Neural Networks
Document Type
Conference Proceeding
Publication Date
7-2-2018
Abstract
The detection of double JPEG compression with the same quantization matrix is a challenging problem in image forensics. In this paper, a CNN framework is proposed to solve this problem. This framework contains a preprocessing layer and a well-designed CNN. In the preprocessing layer, the rounding and truncation error images are extracted from continuous recompressed input samples and then fed into the following CNN. In the design of the CNN architecture, several advanced techniques are carefully considered to prevent overfitting, such as 1\times 1 convolutional kernel and global average pooling layer. The performance of proposed framework is evaluated on the public available image dataset (BOSSbase) with various quality factors (QF). Experimental results have shown the proposed CNN framework performs better than the state-of-the-art method based on hand-crafted features.
Identifier
85063497022 (Scopus)
ISBN
[9789881476852]
Publication Title
2018 Asia Pacific Signal and Information Processing Association Annual Summit and Conference Apsipa ASC 2018 Proceedings
External Full Text Location
https://doi.org/10.23919/APSIPA.2018.8659763
First Page
717
Last Page
721
Grant
61572320
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Peng, Peng; Sun, Tanfeng; Jiang, Xinghao; Xu, Ke; Li, Bin; and Shi, Yunqing, "Detection of Double JPEG Compression with the Same Quantization Matrix Based on Convolutional Neural Networks" (2018). Faculty Publications. 8549.
https://digitalcommons.njit.edu/fac_pubs/8549
