Understanding multi-layer perceptrons on spatial image steganalysis features

This paper proposes to apply single layer perceptrons (SLP) and multi-layer perceptrons (MLP) for image steganalysis. Compared to the popular ensemble classifier (EC) that previous works used, the MLP has larger capacity to fit the training data and improves the test performance accordingly. When we compare different classifiers, the overfitting gap between the training and test results is a good indicator of a classifier's learning ability. On the BOSSbase V1.01 dataset, the experiments demonstrate that the MLP is able to learn a better feature representation than the raw input feature. For instance, on one of the spatial image steganalysis features, a relative performance gain of 2.62% (from 72.58% to 74.48%) is obtained by the MLP over the EC.

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

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

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

[4]  O. Roeva,et al.  Information Hiding: Techniques for Steganography and Digital Watermarking , 2000 .

[5]  Jessica J. Fridrich,et al.  Histogram Layer, Moving Convolutional Neural Networks Towards Feature-Based Steganalysis , 2017, Media Watermarking, Security, and Forensics.

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

[7]  Stefan Duffner,et al.  Pairwise Identity Verification via Linear Concentrative Metric Learning , 2018, IEEE Transactions on Cybernetics.

[8]  J. Mielikainen.  LSB matching revisited , 2006, IEEE Signal Processing Letters.

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

[10]  Tomás Pevný,et al.  Using High-Dimensional Image Models to Perform Highly Undetectable Steganography , 2010, Information Hiding.

[11]  Petra Mutzel,et al.  A Graph-Theoretic Approach to Steganography , 2005, Communications and Multimedia Security.

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

[13]  Jiwu Huang,et al.  Edge Adaptive Image Steganography Based on LSB Matching Revisited , 2010, IEEE Transactions on Information Forensics and Security.

[14]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[15]  Jing Dong,et al.  Learning and transferring representations for image steganalysis using convolutional neural network , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[16]  Raphaël Couturier,et al.  Steganalysis via a Convolutional Neural Network using Large Convolution Filters , 2016, ArXiv.

[17]  Tomás Pevný,et al.  Steganalysis by Subtractive Pixel Adjacency Matrix , 2009, IEEE Transactions on Information Forensics and Security.

[18]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

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

[20]  Andreas Westfeld,et al.  F5-A Steganographic Algorithm , 2001, Information Hiding.

[21]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[22]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[23]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[24]  Marc Chaumont,et al.  Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch , 2015, Media Watermarking, Security, and Forensics.

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

[26]  Bin Li,et al.  Stacked convolutional auto-encoders for steganalysis of digital images , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[27]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[28]  Jian Zhao,et al.  Deep Learning on Spatial Rich Model for Steganalysis , 2016, IWDW.

[29]  Yoshua Bengio,et al.  Understanding intermediate layers using linear classifier probes , 2016, ICLR.

[30]  Kevin Curran,et al.  A skin tone detection algorithm for an adaptive approach to steganography , 2009, Signal Process..

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

[32]  Yan Liu,et al.  Deep residual learning for image steganalysis , 2018, Multimedia Tools and Applications.