Paper Info
Title
Comparison Of Dct And Gabor Filters In Residual Extraction Of Cnn Based Jpeg Steganalysis
Abstract
An effective feature selection method to capture the weak stego noise is essential to image steganalysis. In the conventional JPEG steganalysis, Gabor filter and DCT filter are both used for residual extraction. However, there are few comparisons in existing convolutional neural networks (CNNs) based JPEG steganalysis using Gabor filter or DCT filter in the pre-processing stage to extract residuals. In this paper, we compare the performance of DCT filter with Gabor filter in the pre-processing phase of the steganalysis CNN. Firstly, we choose the parameters empirically and theoretically for Gabor filters which are used in CNN. Secondly, we improve the performance by removing the ABS layer in the original XuNet. Finally, the experimental results show that using Gabor filters or DCT filter can achieve comparable performance whenever the parameters of pre-processing filters are fixed or learnable. It's different from the conventional steganalysis method where Gabor filters have advantages over DCT filters. When the parameters of the pre-processing filters are learnable, both Gabor filter and DCT filter can achieve better performance compared with the condition where the parameters are fixed.
Year
DOI
Venue
2018
10.1007/978-3-030-11389-6_3
DIGITAL FORENSICS AND WATERMARKING, IWDW 2018
Keywords
Field
DocType
JPEG steganalysis, Gabor filter, Convolutional neural networks (CNNs)
Residual,Computer vision,Steganography,Feature selection,Computer science,Convolutional neural network,Discrete cosine transform,Gabor filter,JPEG,Artificial intelligence,Steganalysis
Conference
Volume
ISSN
Citations 
11378
0302-9743
0
PageRank 
References 
Authors
0.34
21
5
Name
Order
Citations
PageRank
Huilin Zheng100.34
Xuan Li212427.25
Danyang Ruan3121.14
Xiangui Kang442437.76
Yun Q. Shi52918199.53