[Paper Review] Reversible Data Hiding in Encrypted Images based on MSB Prediction and Huffman Coding

Youzhi Xiang, Zhaoxia Yin+1 more|arXiv (Cornell University)|Dec 22, 2018

Advanced Steganography and Watermarking Techniques23 references4 citations

TL;DR

This paper proposes a high-capacity reversible data hiding scheme in encrypted images using adaptive MSB prediction and Huffman coding. By predicting and encoding the most significant bits of pixels before encryption, the method creates predictable redundancy that enables efficient embedding of secret data in the encrypted domain while ensuring lossless recovery of both the original image and embedded data.

ABSTRACT

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention as a technology that can embed additional data in the encryption domain. In general, an RDHEI method embeds secret data in an encrypted image while ensuring that the embedded data can be extracted error-free and the original image can be restored lossless. In this paper, A high-capacity RDHEI algorithm is proposed. At first, the Most Significant Bits (MSB) of each pixel was predicted adaptively and marked by Huffman coding in the original image. Then, the image was encrypted by a stream cipher method. At last, the vacated space can be used to embed additional data. Experimental results show that our method achieved higher embedding capacity while comparing with the state-of-the-art methods.

Motivation & Objective

• Address the growing need for secure cloud storage with privacy-preserving data embedding in encrypted images.
• Overcome the limitations of low embedding capacity in existing reversible data hiding in encrypted images (RDHEI) schemes.
• Enable lossless recovery of both the original image and embedded data after extraction.
• Maintain high security by performing data embedding in the encrypted domain without decryption.

Proposed method

• Adopt adaptive prediction of the Most Significant Bits (MSB) of each pixel in the original image to estimate pixel values.
• Apply Huffman coding to the predicted MSB values to compress and mark the prediction errors, creating redundant space.
• Encrypt the image using a stream cipher after MSB prediction and Huffman coding to ensure security in the encrypted domain.
• Utilize the vacated space from the compressed prediction errors to embed secret data in the encrypted image.
• Ensure reversibility by storing prediction and coding information in a way that allows exact reconstruction of the original image.
• Enable extraction of embedded data and full recovery of the original image without any distortion.

Experimental results

Research questions

• RQ1 Can a high-capacity reversible data hiding scheme be designed for encrypted images while preserving lossless recovery?
• RQ2 How can MSB prediction and Huffman coding be combined to maximize embedding capacity in the encrypted domain?
• RQ3 What is the impact of pre-processing via prediction and coding on the efficiency and security of data embedding in encrypted images?
• RQ4 Can the proposed method outperform state-of-the-art RDHEI techniques in terms of embedding capacity and reversibility?
• RQ5 How does the integration of lossless compression (Huffman coding) with encryption enhance the performance of reversible data hiding?

Key findings

• The proposed method achieves a higher embedding capacity compared to state-of-the-art RDHEI schemes.
• The use of adaptive MSB prediction and Huffman coding effectively creates redundant space for data embedding without compromising image quality.
• The original image is restored losslessly after data extraction, confirming the reversibility of the scheme.
• The method maintains strong security by performing data embedding entirely in the encrypted domain.
• The experimental results demonstrate that the proposed approach outperforms existing methods in terms of embedding rate.
• The scheme is validated on standard image datasets, showing consistent performance improvements across various test cases.