A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning. (arXiv:2302.11527v1 [cs.CV])

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 …

algorithm algorithms context datasets images introduction nested networks neural networks performance protocol security smart study variable