IMPORTANCE OF TRUNCATION ACTIVATION IN PRE-PROCESSING FOR SPATIAL AND JPEG IMAGE STEGANALYSIS

In recent years, research on the task of image steganalysis have increasingly tapped on the power of deep learning algorithms to build more complex and deeper CNN to improve detection performance of embedded secrets in both grayscale and/or JPEG images. This paper will present an empirical study on the effectiveness of having a truncation activation function at the pre-processing phase of a CNN-based steganalyzer. Specifically, with commonly-used high-pass filters and the truncation activation, a domain-specific steganalyzer can now be expanded for multi-domain steganalysis, i.e., for both spatial and JPEG domain steganalysis. In our experiments, we investigated two state-of-the-art CNN-based steganalyzers, namely the Yedroudj-Net and Dense-Net which were originally built solely for spatial and JPEG steganalysis respectively. The truncation activation in pre-processing has shown to improve detection rate and accelerate the training phase.