Detection of double JPEG compression using modified DenseNet model

With the increasing tendency of the tempering of JPEG images, development of methods detecting image forgery is of great importance. In many cases, JPEG image forgery is usually accompanied with double JPEG compression, leaving no visual traces. In this paper, a modified version of DenseNet (densely connected convolutional networks) is proposed to accomplish the detection task of primary JPEG compression among double compressed images. A special filtering layer in the front of the network contains typically selected filtering kernels that can help the network following to discriminating the images more easily. As shown in the results, the network has achieved great improvement compared to the-state-of-the-art method especially on the classification accuracy among images with lower quality factors.

[1]  Tomás Pevný,et al.  Detection of Double-Compression in JPEG Images for Applications in Steganography , 2008, IEEE Transactions on Information Forensics and Security.

[2]  Yao Zhao,et al.  Double JPEG Compression Detection by Exploring the Correlations in DCT Domain , 2018, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[3]  Qingzhong Liu,et al.  A Method to Detect JPEG-Based Double Compression , 2011, ISNN.

[4]  Chuan Qin,et al.  Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks , 2017, Symmetry.

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[7]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[8]  Wei Su,et al.  A machine learning based scheme for double JPEG compression detection , 2008, 2008 19th International Conference on Pattern Recognition.

[9]  Jiwu Huang,et al.  A convolutive mixing model for shifted double JPEG compression with application to passive image authentication , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Jian Sun,et al.  Convolutional neural networks at constrained time cost , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Weiming Zhang,et al.  On the fault-tolerant performance for a class of robust image steganography , 2018, Signal Process..

[12]  Tiberio Uricchio,et al.  Tracing images back to their social network of origin: A CNN-based approach , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).

[13]  Kilian Q. Weinberger,et al.  Deep Networks with Stochastic Depth , 2016, ECCV.

[14]  Nitin Khanna,et al.  DCT-domain Deep Convolutional Neural Networks for Multiple JPEG Compression Classification , 2017, Signal Process. Image Commun..

[15]  Jürgen Schmidhuber,et al.  Training Very Deep Networks , 2015, NIPS.

[16]  Tiberio Uricchio,et al.  Localization of JPEG Double Compression Through Multi-domain Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[20]  Weiming Zhang,et al.  Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes , 2016, Multimedia Tools and Applications.

[21]  Belhassen Bayar,et al.  A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer , 2016, IH&MMSec.

[22]  Bin Li,et al.  A multi-branch convolutional neural network for detecting double JPEG compression , 2017, ArXiv.

[23]  Jürgen Schmidhuber,et al.  Highway Networks , 2015, ArXiv.

[24]  Zhenjun Tang,et al.  Expose noise level inconsistency incorporating the inhomogeneity scoring strategy , 2017, Multimedia Tools and Applications.

[25]  Qing Wang,et al.  Double JPEG compression forensics based on a convolutional neural network , 2016, EURASIP J. Inf. Secur..

[26]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.