Prediction error expansion‐based reversible data hiding in encrypted images with public key cryptosystem

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.

Section: Image Encryptionmentioning

confidence: 99%

“…Therefore, data security has attracted much attention in the past years. Reversible data hiding (RDH) [1][2][3][4] is a useful technique of data security. It can embed secret information into multimedia carrier, e.g.…”

Section: Introductionmentioning

confidence: 99%

Reversible data hiding for encrypted image based on adaptive prediction error coding

Tang

Pang

et al. 2021

“…They cannot identify the privacy sensitivity differences among instances in an image. Inspired by the fact that the problem of differential privacy has been considered in the field of data processing [21,22], we believe it is necessary to measure the privacy levels of the instances in an image. And it is valuable to research the contribution of visual information on the privacy measurement.…”

Section: Introductionmentioning

confidence: 99%

Reliable metrics‐based linear regression model for multilevel privacy measurement of face instances

Huang

et al. 2022

Social networking sites have made photo sharing convenient and, consequently, users of those sites frequently share photos. The proliferation of these social images throughout the Internet has inadvertently exposed an increasing number of personally identifiable information, particularly from the visual information of the faces in the images. Most existing methods to de-identify faces results in an excessive loss of visual information. To solve this problem, this paper proposes a reliable metrics-based linear regression model for multilevel privacy measurement of face instances using the size information of face instances. The proposed privacy measurement model provides a novel instance-level-based solution to measure privacy levels. The paper also establishes a scientific relationship between the size information of face instances and their privacy levels, quantifying the degree to which face instances need to be de-identified. Finally, the paper proposes a novel k-Same-DT de-identification method to provide reliable metrics for a linear regression model. It is a real-time k-same-related de-identification algorithm that combines the PCA dimensionality reduction strategy and the Delaunay triangle-based face alignment algorithm.The proposed k-Same-DT method creates a de-identified face with a lower identifiable rate and higher structural similarity, and it can provide reliable de-identification metrics for privacy measurement. Experiments using the classical face dataset demonstrates the effectiveness of the proposed de-identification method. Extensive experiments and surveys on real-world social images were also conducted to verify the proposed measurement model.

“…To solve this problem, reversible data hiding (RDH) [2] is introduced, which can ensure the privacy and extractability of embedded information, and can completely recover the cover medium without damage. Nowadays, many RDH algorithms are proposed, including lossless compression [3][4][5][6], histogram shifting (HS) [7][8][9][10], difference expansion (DE) [11] and prediction error expansion (PEE) [12][13][14][15] and so on. The main idea of compression is loss-less compression of the content of the cover medium, such as the least significant bit (LSB) and embed secret information in the space saved.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, many RDH algorithms are proposed, including lossless compression [3][4][5][6], histogram shifting (HS) [7][8][9][10], difference expansion (DE) [11] and prediction error expansion (PEE) [12][13][14][15] and so on. The main idea of compression is loss-less compression of the content of the cover medium, such as the least significant bit (LSB) and embed secret information in the space saved.…”

Section: Introductionmentioning

confidence: 99%

A novel reversible data hiding based on adaptive block‐partition and payload‐allocation method

Yang

Huang

Zou

et al. 2020

Self Cite

A reversible data hiding method based on partition is researched because it can effectively reduce shifting distortion in given embedding capacity. However, traditional partition is to divide an image into equal-sized blocks, which cannot be divided reasonably according to the content of the image. In order to achieve dynamic partition and effectively utilize the complexity of the image, this paper proposes a novel reversible data hiding based on adaptive block-partition and payload-allocation method. In this technique, instead of equal partition, adaptive block-partition is proposed to establish multiple histograms by dividing the cover image into several image blocks of different sizes dynamically, and the image blocks of different sizes are processed successively through multiple sorting and implement adaptive payload allocation according to complexity, then the data is embedded into two sides of prediction-error histograms to effectively reduce the shifting distortion. Experimental results show that the proposed method is superior to the state-of-the-art traditional fixed-sized blocking-based reversible data hiding methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.