PPE-Based Reversible Data Hiding

Wu, Hanzhou; Wang, Hongxia; Shi, Yun-Qing

doi:10.1145/2909827.2933196

Cited by 22 publications

(21 citation statements)

References 16 publications

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“…We need to generate an ordered PE sequence for embedding m. It is quite desirable to further sort the PEs according to their local-complexities such that small PEs can be used first. This perspective has been utilized in many existing works [11], [14]. We use the prediction values of pixels in S .…”

Section: Data Embeddingmentioning

confidence: 99%

“…It inspires researchers to design prediction based RDH algorithms, which use the prediction-errors (PEs) to carry a payload. A common operation [11], [24] used in RDH is therefore to generate a prediction error histogram (PEH) for data embedding. On the one hand, the PEs are noise-like components of the cover, meaning that, the slight modification to PEs would not result in obvious artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Reversible Data Hiding Simultaneously Exploiting Adjacent Pixels

2020

IEEE Access

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As an effective means to content authentication and privacy protection, reversible data hiding (RDH) permits us to hide a payload such as authentication data in a media file. The resulting marked content will not introduce noticeable artifacts. In order to achieve superior payload-distortion performance, the conventional RDH algorithms often exploit the smooth content for data embedding. Since RDH allows both the embedded payload and the raw content to be perfectly reconstructed, it is required that, the altered smooth regions within the cover should be identified without error by a data receiver. Therefore, a core work in RDH is to design an efficient content-aware algorithm that can enable a data hider to take advantages of the smooth cover elements as much as possible while the detection procedure for the marked elements should be invertible to a data receiver. This has motivated the authors to present a novel patch-level selection and breadth-first prediction strategy for efficient RDH in this paper. However, different from many conventional RDH works, the proposed approach allows a data hider to preferentially and simultaneously use adjacent smooth elements as many as possible, which can benefit data embedding procedure a lot. Experiments show that our work significantly outperforms a part of advanced RDH algorithms in terms of the payload-distortion performance, which has demonstrated the superiority and applicability. INDEX TERMS Reversible data hiding, watermarking, fragile, patch, breadth-first search.

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Section: Data Embeddingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Reversible Data Hiding Simultaneously Exploiting Adjacent Pixels

2020

IEEE Access

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“…Since HS has been a most popular operation in RDH, we will use HS-based RDH algorithms for experiments. In detail, four state-of-the-art algorithms, i.e., Ni et al [2], Li et al [6], Wu et al [9] and Hsu et al [4], are used to construct an ensemble RDH system. There are three reasons for why we select the four algorithms.…”

Section: Detailed Ensemble Rdh Schemementioning

confidence: 99%

“…For a given image, we change m from 2 to 8 by a step value of 1 for optimization. The best rate-distortion performance will be considered as the result since the data sender always has the freedom to choose better [9], and the proposed ensemble algorithm for the six standard test images. We here vary m from 2 to 8 for rate-distortion optimization during each-layer embedding (N = m 2 ).…”

Section: Performance Evaluation and Analysismentioning

confidence: 99%

Ensemble Reversible Data Hiding

Wu¹,

Wang²,

Wang³

2018

2018 24th International Conference on Pattern Recognition (ICPR)

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The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a secret payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the ratedistortion optimization view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for efficient RDH in this paper, in which the raw host could be partitioned into multiple subhosts such that each one can freely optimize and use the data embedding parameters. Moreover, it enables us to apply different RDH algorithms within different subhosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of the proposed work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system could outperform the original versions in most cases, which has shown the superiority and applicability.

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“…More efficient approaches are introduced to increase the data embedding capacity or reduce the distortion such as difference expansion [1], histogram shifting [2] and other methods [3], [4]. Nowadays, advanced algorithms use prediction-errors (PEs) [5]- [9] of the cover to hide secret data since PEs could provide superior rate-distortion performance.…”

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confidence: 99%

Reversible Embedding to Covers Full of Boundaries

Wu¹,

Wang²,

Chen³

et al. 2018

Cloud Computing and Security

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In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed. The existing algorithms often assume that a natural image has little boundary pixels so that the size of side information is small. Accordingly, a relatively high pure payload could be achieved. However, there actually may exist a lot of boundary pixels in a natural image, implying that, the size of side information could be very large. Therefore, when to directly use the existing algorithms, the pure embedding capacity may be not sufficient. In order to address this problem, in this paper, we present a new and efficient framework to reversible data embedding in images that have lots of boundary pixels. The core idea is to losslessly preprocess boundary pixels so that it can significantly reduce the side information. Experimental results have shown the superiority and applicability of our work.

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PPE-Based Reversible Data Hiding

Cited by 22 publications

References 16 publications

Efficient Reversible Data Hiding Simultaneously Exploiting Adjacent Pixels

Efficient Reversible Data Hiding Simultaneously Exploiting Adjacent Pixels

Ensemble Reversible Data Hiding

Reversible Embedding to Covers Full of Boundaries

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