A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning
This addresses the problem of inconsistent steganalysis performance for security applications when dealing with variable image dimensions, representing an incremental improvement in the field.
The study investigated the performance invariance of convolutional neural networks in steganalysis across varying image sizes, revealing that invariance does not exist in state-of-the-art architectures and showing differences in behavior based on image size comparisons. It proposed using dilated convolution, which improved a state-of-the-art architecture based on experimental results.
In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more "wild steganalysis". First, we propose two algorithms and definitions for a fine experimental protocol with datasets owning "similar difficulty" and "similar security". The "smart crop 2" algorithm allows the introduction of the Nearly Nested Image Datasets (NNID) that ensure "a similar difficulty" between various datasets, and a dichotomous research algorithm allows a "similar security". Second, we show that invariance does not exist in state-of-the-art architectures. We also exhibit a difference in behavior depending on whether we test on images larger or smaller than the training images. Finally, based on the experiments, we propose to use the dilated convolution which leads to an improvement of a state-of-the-art architecture.