# Yedrouj-Net: An efficient CNN for spatial steganalysis

arXiv: https://arxiv.org/abs/1803.00407 · HTML: https://mlsanity.com/paper/1803.00407/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 19 of 19)

- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Universal Deep Network for Steganalysis of Color Image based on Channel Representation](https://mlsanity.com/paper/2111.12231/) (2021)
- [Diffusion-Based Hierarchical Image Steganography](https://mlsanity.com/paper/2405.11523/) (2024)
- [MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography](https://mlsanity.com/paper/2103.13689/) (2021)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge](https://mlsanity.com/paper/2105.03867/) (2021)
- [FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack](https://mlsanity.com/paper/2106.06895/) (2021)
- [Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks](https://mlsanity.com/paper/2201.07444/) (2022)
- [Green Steganalyzer: A Green Learning Approach to Image Steganalysis](https://mlsanity.com/paper/2306.04008/) (2023)
- [Stego Battlefield: Evaluating Image Steganography Attacks and Steganalysis Defenses](https://mlsanity.com/paper/2605.05789/) (2026)
- [Using contrastive learning to improve the performance of steganalysis schemes](https://mlsanity.com/paper/2103.00891/) (2021)
- [Multi-Contextual Design of Convolutional Neural Network for Steganalysis](https://mlsanity.com/paper/2106.10430/) (2021)
- [Pixel-Stega: Generative Image Steganography Based on Autoregressive Models](https://mlsanity.com/paper/2112.10945/) (2021)
- [Steganalysis of Image with Adaptively Parametric Activation](https://mlsanity.com/paper/2203.12843/) (2022)
- [STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition](https://mlsanity.com/paper/2206.05651/) (2022)
- [A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning](https://mlsanity.com/paper/2302.11527/) (2023)
- [CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques](https://mlsanity.com/paper/2304.12503/) (2023)
- [PENet+: A Lightweight Residual Transformer Framework for Efficient Image Steganalysis](https://mlsanity.com/paper/2606.10939/) (2026)

## Abstract (arXiv)

For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.