Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. ,  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 ll parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments re conducted in order to find the best " shape " of the CNN. Second, experiments re 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 re 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,  provide discussions on the internal mechanisms of a CNN, and ave links with some previously stated ideas, in order to understand the impressive results  obtained.
['Lionel Pibre', 'Pasquet Jérôme', 'Dino Ienco', 'Marc Chaumont']
I am writing a research paper on a idea Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch
[{"from": "human", "value": "I am writing a research paper on a idea Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. ,  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 ll parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments re conducted in order to find the best \" shape \" of the CNN. Second, experiments re 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 re 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,  provide discussions on the internal mechanisms of a CNN, and ave links with some previously stated ideas, in order to understand the impressive results  obtained."}]