Local-Source Enhanced Residual Network for Steganalysis of Digital Images

Steganalysis refers to the study of identifying hidden messages in images inserted by steganography. Although detection performance is greatly improved when adopting convolutional neural networks (CNNs), they require sophisticated tricks, such as preprocessing for suppression of image content, using absolute and truncated activation functions, and utilizing domain knowledge. These tricks help networks train stably and mitigate the convergence problem of early stages in training, but they also restrict the flexibility of CNNs, which limits their performance. In this paper, we propose a local-source enhanced residual network (LSER) with end-to-end learning. The LSER is simple in its architecture but has two distinct characteristics from previous methods. First, the LSER uses residual blocks without any normalization. We find batch normalization is an unnecessary module in our framework. Second, a local-source skip connection is added to bypass features of different levels, which allows more abundant feature representation. Moreover, the LSER exhibits state-of-the-art results compared with the existing work in both spatial and JPEG domain steganalysis. Furthermore, a simple self-ensemble method further improves its performance without any side information.

[1]  Nasir D. Memon,et al.  Image Steganalysis with Binary Similarity Measures , 2005, EURASIP J. Adv. Signal Process..

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

[3]  Jiangqun Ni,et al.  A deep learning approach to detection of splicing and copy-move forgeries in images , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

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

[5]  Mo Chen,et al.  Deep Residual Network for Steganalysis of Digital Images , 2019, IEEE Transactions on Information Forensics and Security.

[6]  Jinseok Park,et al.  Content-Aware Image Resizing Detection Using Deep Neural Network , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[7]  Larry S. Davis,et al.  Learning Rich Features for Image Manipulation Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Yun Fu,et al.  Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Yun Q. Shi,et al.  Uniform Embedding for Efficient JPEG Steganography , 2014, IEEE Transactions on Information Forensics and Security.

[10]  Jessica Fridrich,et al.  Reverse JPEG Compatibility Attack , 2020, IEEE Transactions on Information Forensics and Security.

[11]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[12]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[13]  Jessica Fridrich,et al.  Steganography in Digital Media: References , 2009 .

[14]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[15]  Ebroul Izquierdo,et al.  Advanced Super-Resolution Using Lossless Pooling Convolutional Networks , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[16]  Jianyi Liu,et al.  Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis , 2020, IEEE Transactions on Information Forensics and Security.

[17]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.

[18]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[19]  Jessica J. Fridrich,et al.  Breaking ALASKA: Color Separation for Steganalysis in JPEG Domain , 2019, IH&MMSec.

[20]  Yan Liu,et al.  A Novel Convolutional Neural Network for Image Steganalysis With Shared Normalization , 2017, IEEE Transactions on Multimedia.

[21]  Guanshuo Xu,et al.  Deep Convolutional Neural Network to Detect J-UNIWARD , 2017, IH&MMSec.

[22]  Yun Q. Shi,et al.  Structural Design of Convolutional Neural Networks for Steganalysis , 2016, IEEE Signal Processing Letters.

[23]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[24]  Jessica J. Fridrich,et al.  Selection-channel-aware rich model for Steganalysis of digital images , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[25]  Siwei Lyu,et al.  Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines , 2002, Information Hiding.

[26]  Marc Chaumont,et al.  Yedroudj-Net: An Efficient CNN for Spatial Steganalysis , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[27]  Patrick Bas,et al.  The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis , 2019, IH&MMSec.

[28]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[29]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[30]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[31]  Jessica J. Fridrich,et al.  Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[32]  Qilong Wang,et al.  Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[33]  Teddy Furon,et al.  Broken Arrows , 2008, EURASIP J. Inf. Secur..

[34]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Marc Chaumont,et al.  Camera model identification with the use of deep convolutional neural networks , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[36]  Tomás Pevný,et al.  "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.

[37]  Jiangqun Ni,et al.  Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.

[38]  Jiwu Huang,et al.  Adaptive steganalysis against WOW embedding algorithm , 2014, IH&MMSec '14.

[39]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[40]  Mo Chen,et al.  JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images , 2017, IH&MMSec.

[41]  Jiwu Huang,et al.  Adaptive Steganalysis Based on Embedding Probabilities of Pixels , 2016, IEEE Transactions on Information Forensics and Security.

[42]  Shu-Tao Xia,et al.  Second-Order Attention Network for Single Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Jessica Fridrich,et al.  Detection of Diversified Stego Sources with CNNs , 2019, Media Watermarking, Security, and Forensics.

[44]  Bolin Chen,et al.  Fast and Effective Global Covariance Pooling Network for Image Steganalysis , 2019, IH&MMSec.

[45]  Jessica J. Fridrich,et al.  Steganalysis Features for Content-Adaptive JPEG Steganography , 2016, IEEE Transactions on Information Forensics and Security.

[46]  Jingwen Yan,et al.  A Customized Convolutional Neural Network with Low Model Complexity for JPEG Steganalysis , 2019, IH&MMSec.

[47]  Jing Dong,et al.  Deep learning for steganalysis via convolutional neural networks , 2015, Electronic Imaging.