Refactoring a Full Stack Web Application to Remove Barriers for Student Developers and to Add Customization for Instructors

Cook, Jack; Weiss, Richard; Mache, Jens

In this paper, we investigate the effect of pretraining CNNs on Ima- geNet on their performance when refined for steganalysis of digital images. In many cases, it seems that just ’seeing’ a large number of images helps with the convergence of the network during the refinement no matter what the pretraining task is. To achieve the best performance, the pretraining task should be related to steganal- ysis, even if it is done on a completely mismatched cover and stego datasets. Furthermore, the pretraining does not need to be carried out for very long and can be done with limited computational re- sources. An additional advantage of the pretraining is that it is done on color images and can later be applied for steganalysis of color and grayscale images while still having on-par or better perfor- mance than detectors trained specifically for a given source. The refining process is also much faster than training the network from scratch. The most surprising part of the paper is that networks pretrained on JPEG images are a good starting point for spatial domain steganalysis as well.

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