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Improved CNN Prediction Based Reversible Data Hiding for Images.

Yingqiang Qiu¹, Wanli Peng², Xiaodan Lin¹

¹College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China.

Entropy (Basel, Switzerland)

|February 26, 2025

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This study introduces an improved convolutional neural network predictor (ICNNP) for reversible data hiding in images. The ICNNP enhances performance by predicting pixel complexity for more efficient data embedding.

Area of Science:

Computer Vision
Image Processing
Cryptography

Background:

Reversible data hiding (RDH) is crucial for embedding secret information within images without permanent distortion.
Existing methods using convolutional neural network predictors (CNNP) show promise but can be improved for better rate-distortion performance.

Purpose of the Study:

To propose an improved convolutional neural network predictor (ICNNP) for enhanced reversible data hiding in images.
To achieve superior rate-distortion performance compared to existing CNNP-based schemes.

Main Methods:

The proposed ICNNP consists of feature extraction, pixel prediction, and complexity prediction modules.
Images are split into sub-images, and the ICNNP predicts one sub-image from another.
Prediction errors are sorted by complexity, and less complex errors are used for data embedding via histogram shifting.

Keywords:

CNN histogram shifting multitasking reversible data hiding

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Main Results:

The ICNNP effectively predicts pixel complexity, enabling more efficient data embedding.
The proposed scheme demonstrates superior rate-distortion performance over a standard CNNP-based scheme.
Experimental results validate the effectiveness of the ICNNP for RDH.

Conclusions:

The developed ICNNP offers an effective approach for reversible data hiding with improved performance.
Predicting pixel complexity is key to achieving low-distortion and high-capacity data embedding.
The ICNNP represents a significant advancement in image steganography techniques.