# Universal Deep Network for Steganalysis of Color Image based on Channel Representation

arXiv: https://arxiv.org/abs/2111.12231 · HTML: https://mlsanity.com/paper/2111.12231/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [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)
- [WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images](https://mlsanity.com/paper/1803.04805/) (2018)
- [Analysis of the Scalability of a Deep-Learning Network for Steganography "Into the Wild"](https://mlsanity.com/paper/2012.14816/) (2020)

## Built on by (most cited first, 5 of 5)

- [AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream Network](https://mlsanity.com/paper/2306.07005/) (2023)
- [Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography](https://mlsanity.com/paper/2409.04878/) (2024)
- [Towards Imperceptible JPEG Image Hiding: Multi-range Representations-driven Adversarial Stego Generation](https://mlsanity.com/paper/2507.08343/) (2025)
- [Rethinking Security of Diffusion-based Generative Steganography](https://mlsanity.com/paper/2602.10219/) (2026)
- [Efficient Cross-Scale Invertible Hiding Network with Spatial-Frequency Collaboration and Non-Invertible Mechanism](https://mlsanity.com/paper/2606.25547/) (2026)

## Abstract (arXiv)

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design.