Data Augmentation for JPEG Steganalysis

Abstract

Deep Convolutional Neural Networks (CNNs) have performed remarkably well in JPEG steganalysis. However, they heavily rely on large datasets to avoid overfitting. Data augmentation is a popular technique to inflate the datasets available without collecting new images. For JPEG steganalysis, the augmentations predominantly used by researchers are limited to rotations and flips (D4 augmentations). This is due to the fact that the stego signal is erased by most augmentations used in computer vision. In this paper, we systematically survey a large number of other augmentation techniques and assess their benefit in JPEG steganalysis.