# Deep Convolutional Neural Network to Detect J-UNIWARD

arXiv: https://arxiv.org/abs/1704.08378 · HTML: https://mlsanity.com/paper/1704.08378/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2017.

## Built on by (most cited first, 10 of 10)

- [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)
- [MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography](https://mlsanity.com/paper/2103.13689/) (2021)
- [Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge](https://mlsanity.com/paper/2105.03867/) (2021)
- [JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning](https://mlsanity.com/paper/2302.02276/) (2023)
- [Implicit Steganography Beyond the Constraints of Modality](https://mlsanity.com/paper/2312.05496/) (2023)
- [Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image](https://mlsanity.com/paper/2101.05209/) (2021)
- [Using contrastive learning to improve the performance of steganalysis schemes](https://mlsanity.com/paper/2103.00891/) (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)

## Abstract (arXiv)

This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512x512. Results have verified that both the pooling method and the depth of the CNNs are critical for performance. Results have also proved that a 20-layer CNN, in general, outperforms the most sophisticated feature-based methods, but its advantage gradually diminishes on hard-to-detect cases. To show that the performance generalizes to large-scale databases and to different cover sizes, one experiment has been conducted on the CLS-LOC dataset of ImageNet containing more than one million covers cropped to unified size of 256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently proposed for large-scale JPEG steganalysis by 35%. Source code is available via GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysis