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2023, Volume 24, Issue 8

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Frontiers of Information Technology & Electronic Engineering >> 2023, Volume 24, Issue 8 doi: 10.1631/FITEE.2300041

Reversible data hiding using a transformer predictor and an adaptive embedding strategy

Affiliation(s): School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100084, China; School of Information Science & Technology, University of International Relations, Beijing 100091, China; less

Received: 2023-01-20 Accepted: 2023-08-29 Available online: 2023-08-29
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Abstract

In the field of (RDH), designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects. In this paper, we propose a new RDH method, including a predictor based on a and a novel embedding strategy with multiple embedding rules. In the predictor part, we first design a -based predictor. Then, we propose an image division method to divide the image into four parts, which can use more pixels as context. Compared with other predictors, the -based predictor can extend the range of pixels for prediction from neighboring pixels to global ones, making it more accurate in reducing the embedding distortion. In the embedding strategy part, we first propose a complexity measurement with pixels in the target blocks. Then, we develop an improved prediction error ordering rule. Finally, we provide an embedding strategy including multiple embedding rules for the first time. The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images, and experimental results show that the performance of our RDH method is leading the field.

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