Paper Info
Title
Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch
Abstract
Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best " shape " of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained.
Year
Venue
Field
2015
CoRR
Parameterized complexity,Convolutional neural network,Computer science,Feature extraction,Minification,Artificial intelligence,Steganalysis,Deep learning,Artificial neural network,Classifier (linguistics)
DocType
Volume
Citations 
Journal
abs/1511.04855
4
PageRank 
References 
Authors
0.50
22
4
Name
Order
Citations
PageRank
lionel pibre140.50
Jerome Pasquet2243.20
Dino Ienco329542.01
Marc Chaumont417220.40