Paper Info
Title
Detection of Double JPEG Compression with the Same Quantization Matrix Based on Convolutional Neural Networks
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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> 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.
Year
DOI
Venue
2018
10.23919/APSIPA.2018.8659763
2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Keywords
Field
DocType
Image Forensics,Double JPEG Compression,the Same Quantization Matrix,Convolutional Neural Network
Kernel (linear algebra),Truncation error,Pattern recognition,Convolutional neural network,Computer science,Pooling,Rounding,Preprocessor,Artificial intelligence,Overfitting,Quantization matrix
Conference
ISSN
ISBN
Citations 
2309-9402
978-9-8814-7685-2
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Peng Peng1174.78
Tanfeng Sun214125.35
Xinghao Jiang317627.66
Ke Xu491.92
Bin Li552429.42
Yun Q. Shi62918199.53