Mean value based reversible data hiding in encrypted images

Agrawal, Smita; Kumar, Manoj

doi:10.1016/j.ijleo.2016.11.059

Cited by 53 publications

(37 citation statements)

References 35 publications

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“…Figure 7 is the PSNR comparison among the assessed algorithms under different test images when r=1. It is observed that the PSNRs of the proposed algorithm are bigger than those of the compared algorithms [26,27,30] under any embedding rate for all test images. The RDH algorithm reported in [27] has lower performance than other algorithms due to the fact that it embeds several bits into hundreds of pixels.…”

Section: Performance Comparisonsmentioning

confidence: 89%

“…In this section, we compare the proposed algorithm with some recent popular RDH algorithms [22,26,27,30,31]. Figure 7 is the PSNR comparison among the assessed algorithms under different test images when r=1.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…Some researchers [24][25][26][27] have proposed the idea of preprocessing image before encryption for data embedding. For example, Ma et al [24] provided an RDH algorithm in encrypted images by reserving room before encryption.…”

Section: Introductionmentioning

confidence: 99%

“…In another work, Nguyen et al [26] used half of the pixels to classify the rest of the pixels into smooth and complex regions to provide room for embedding additional data. Agrawal and Kumar [27] divided original image into several consecutive sets, calculated mean of each set, stored it at the first element of its set before encryption, and finally embedded a secret bit into the encrypted pixels of one set except for the pixel holding the mean value in the set.…”

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

Zhang

Tang

et al. 2018

Security and Communication Networks

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Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.

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Section: Performance Comparisonsmentioning

confidence: 89%

Section: Performance Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

Zhang

Tang

et al. 2018

Security and Communication Networks

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“…At last, the receiver attained the capability to extract the data that were embedded and recuperated the original JPEG bit stream separately. Agarwal [11] introduced a new RDH method for encoded images. For encryption operation, iimplemented method exploited additive modulo 256 and possessions of average for watermark embedding in the images that are encrypted.…”

Section: A State-of-the-art Of Contributionsmentioning

confidence: 99%

Route Map of Privacy Preservation to IOT

Kathavate¹,

Amudhavel²

2018

IJET

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Data anonymization is the main feature of privacy preservation, and it assists in eradicating the privacy hazard in data preparation in various applications including IoT. Pseudonymity and Anonymization are two significant security factors that were adopted when sensitive data are shared. In medical field, data is usually distributed horizontally with diverse regions carrying a similar set of characteristics for various anonymization techniques.

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