research-article

Reversible data hiding in encrypted images based on pixel-level masked autoencoder and polar code

Authors:

Zhangpei Cheng,

Qingxiao GuanAuthors Info & Claims

Volume 226, Issue C

https://doi.org/10.1016/j.sigpro.2024.109664

Published: 18 November 2024 Publication History

Highlights

•

A novel pixel-level mask autoencoder (PLMAE) is proposed to build a high-performance image recovery mechanism.

•

The idea of channel coding is used in the data embedding mechanism to take full advantage of PLMAE.

•

Compared to state-of-the-art methods, the proposed method significantly improves the embedding rate.

Abstract

In the study of vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), pixel prediction is an important mechanism to achieve reversibility, which has a crucial impact on the capacity and fidelity. In this paper, we propose a novel pixel-level masked autoencoders (PLMAE) as a high-performance pixel predictor for RDHEI. Unlike the original masked autoencoders (MAE), PLMAE focuses on pixel-level reconstruction rather than semantic patch-level reconstruction. The purpose of PLMAE is to spare more carrier pixels while maintaining relatively high prediction accuracy, thereby improving the RDHEI capacity. Based on PLMAE, a novel RDHEI method is proposed. In the proposed method, the data hider encodes the secret data using a polar code and then embeds the encoded data. After the image is decrypted, the receiver considers the carrier pixels as masked pixels, predicts the original states of the carrier pixels using PLMAE to extract the secret data, and then decodes the secret data and recovers the image based on the decoding results. The experimental results demonstrate that the proposed method in this paper can achieve better performance than the existing methods.

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Reversible data hiding in encrypted images based on pixel-level masked autoencoder and polar code

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Information & Contributors

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Published In

cover image Signal Processing

Signal Processing Volume 226, Issue C

Jan 2025

327 pages

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Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 18 November 2024

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