# Steganalysis of Image with Adaptively Parametric Activation

arXiv: https://arxiv.org/abs/2203.12843 · HTML: https://mlsanity.com/paper/2203.12843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Yedrouj-Net: An efficient CNN for spatial steganalysis](https://mlsanity.com/paper/1803.00407/) (2018)
- [Deep Convolutional Neural Network to Detect J-UNIWARD](https://mlsanity.com/paper/1704.08378/) (2017)
- [Large-scale JPEG steganalysis using hybrid deep-learning framework](https://mlsanity.com/paper/1611.03233/) (2016)
- [A Novel Convolutional Neural Network for Image Steganalysis with Shared Normalization](https://mlsanity.com/paper/1711.07306/) (2017)

## Abstract (arXiv)

Steganalysis as a method to detect whether image contains se-cret message, is a crucial study avoiding the imperils from abus-ing steganography. The point of steganalysis is to detect the weak embedding signals which is hardly learned by convolution-al layer and easily suppressed. In this paper, to enhance embed-ding signals, we study the insufficiencies of activation function, filters and loss function from the aspects of reduce embedding signal loss and enhance embedding signal capture ability. Adap-tive Parametric Activation Module is designed to reserve nega-tive embedding signal. For embedding signal capture ability enhancement, we add constraints on the high-pass filters to im-prove residual diversity which enables the filters extracts rich embedding signals. Besides, a loss function based on contrastive learning is applied to overcome the limitations of cross-entropy loss by maximum inter-class distance. It helps the network make a distinction between embedding signals and semantic edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD for experiments. Compared to state-of-the-art methods, our method has a competitive performance.