Double linear regression prediction based reversible data hiding in encrypted images

Li, Fengyong; Zhu, Hengjie; Yu, Jiang; Qin, Chuan

doi:10.1007/s11042-020-09805-6

Cited by 18 publications

(22 citation statements)

References 28 publications

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“…As for EC, our method can reach 0.6625 bpp for both Lean and Baboon. It is clear that our method can obtain a higher payload than other related methods, except for Cao et al [15] and Li et al [16]. These show that our approach provides a good trade-off between the embedding capacity and PSNR.…”

Section: Resultsmentioning

confidence: 56%

“…for these images are shown in Table 2. For embedding capacity, the best case We now compare the performance of our approach with some state-of-theart design [11,13,15,16,17]. Our experiments are based upon the two typical images of Lena and Baboon.…”

Section: Resultsmentioning

confidence: 99%

“…More secret data can be embedded into the vacated room in the encrypted image. In [16], a new schema is proposed by designing double linear regression prediction model, which further improves the prediction accuracy and provides a large embedding capacity. The secret message is embeded by referring to difference between current pixel and its predicted value.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel reversible data hiding in encrypted images based on polynomial arithmetic

Chen¹,

Jian-zhu²,

Huang³

et al. 2021

Preprint

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Reversible data hiding in encrypted images is an effective technique for data hiding and preserving image privacy. In this paper, we propose a novel schema based on polynomial arithmetic, which achieves a high embedding capacity with the perfect recovery of the original image. An efficient two-layer symmetric encryption method is applied to protect the privacy of the original image. One polynomial is generated by the encryption key and a group of the encrypted pixel, and the secret data is mapped into another polynomial. Through the arithmetic of these two polynomials, the purpose of this work is achieved. Furthermore, pixel value mapping is designed to reduce the size of auxiliary data, which can further improve embedding capacity. Experimental results demonstrate that our solution has a stable and good performance on various images.Compared with some state-of-the-art methods, the proposed method can get better decrypted image quality with a large embedding capacity.

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Section: Resultsmentioning

confidence: 56%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel reversible data hiding in encrypted images based on polynomial arithmetic

Chen¹,

Jian-zhu²,

Huang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Security Analysis. To prove that the proposed RDH method can provide the imperceptibility of secret data and stego-medical-image, security analyses, including pixel value difference (PVD) histogram [32], Shannon entropy, the number of pixels change rate (NPCR) [33], and the unified average changing intensity (UCAI) [33], were used to evaluate the stego-medical-image with full payload under ε � 1, pos � 4, h � 2 and w � 2.…”

Section: Resultsmentioning

confidence: 99%

Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation

Lin

Chang

2021

Journal of Healthcare Engineering

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As digitalization becomes more common, patients’ concerns about the leakage of private information, such as electronic medical record, are increasing, and those concerns motivated this case study of secure covert communication. Therefore, in this paper, a novel reversible data hiding method based on pixel rotation is proposed for medical images. Using pixel rotation, a state mapping model is constructed to represent the payload. More specifically, many intermediate states are derived from an image block, and each of them is used to form a one-to-one mapping relationship with a specific sequence of payload bits. In addition, to ensure the visual quality of stego-medical-images, the payload bits are only concealed in the regular blocks and the other blocks are unchanged. Moreover, the smoother regular image block will be priority to be used to embed the payload to enhance the visual quality of stego-medical-image. The experimental results showed that the stego-medical-images generated by the proposed reversible data hiding method have better visual quality with an average PSNR of 47.0307 dB, which is higher than that provided by some state-of-the-art methods.

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“…However, due to the low prediction accuracy of this method, the embedding ability is not improved significantly. Li et al [22] designed a double linear regression prediction model and set a fixed threshold. However, if the threshold setting in the prediction model is not appropriate, it will cause a large difference between the original pixel and its predicted value.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction-error Label Map

Yu¹,

Qin²,

Tan³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In the field of reversible data hiding in encrypted images (RDH-EI), predict an image effectively and embed a message into the image with lower distortion are two crucial aspects. However, due to the linear regression prediction being sensitive to outliers, it is a challenge to improve the accuracy of predictions. To address this problem, this paper proposes an RDH-EI scheme based on adaptive prediction-error label map. In the prediction stage, an adaptive threshold estimation algorithm based on local complexity is proposed. Then, the pixels selection method based on gradient of image is designed to train the parameters of the prediction model. In the embedding stage, reserve enough space to embed auxiliary information and secret data embedding by flipping the least significant bits (LSBs) to encrypt the original image. In the receiver, based on the prediction-error map, error-free image recovery can be achieved. Extensive experimental results have shown that the proposed method can achieve effective pixel prediction results and get a higher embedding rate (ER).

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Double linear regression prediction based reversible data hiding in encrypted images

Cited by 18 publications

References 28 publications

A novel reversible data hiding in encrypted images based on polynomial arithmetic

A novel reversible data hiding in encrypted images based on polynomial arithmetic

Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction-error Label Map

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