Double-threshold reversible data hiding

Xuan, Guixin; Shi, Yun Q.; Teng, Jianzhong; Tong, Xuefeng; Chai, Peiqi

doi:10.1109/iscas.2010.5537323

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…The experiments reported in [53] indicated significant improvement over the original DE. In some recent works [40], [44], [46], [48], [49], [54]- [60], the authors illustrated that combining adaptive embedding strategy such as sorting or pixel-selection with other reversible techniques such as PEE, can dramatically improve the embedding performance. The basic idea of adaptive embedding is to utilize smooth image pixels to derive a better cover medium and then implement reversible data embedding by modifying only the generated cover medium which is a subset of the cover image.…”

Section: ) Prediction-error Expansion (Pee)mentioning

confidence: 99%

“…Another type of improved methods exploited an optimal expansion-bins-selection mechanism for HS-PEE [54], [78]- [87]. An illustration of this mechanism is shown in Fig.…”

Section: ) Recent Advance In Hs-peementioning

confidence: 99%

“…• Adaptive embedding strategies considering local image properties such as sorting [40], [53] or embeddinglocation-selection [54], [55], [81] are very helpful for improving the reversible embedding performance. Generally speaking, adaptive embedding can be incorporated into most RDH schemes to achieve better performance.…”

Section: F Summary and Concluding Remarksmentioning

confidence: 99%

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Reversible data hiding: Advances in the past two decades

et al. 2016

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In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this survey paper the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain, 2) RDH into image compressed domain (e.g., JPEG), 3) RDH suitable for image semi-fragile authentication, 4) RDH with image contrast enhancement, 5) RDH into encrypted images, which is expected to have wide application in the cloud computation, and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

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Section: ) Prediction-error Expansion (Pee)mentioning

confidence: 99%

“…Another type of improved methods exploited an optimal expansion-bins-selection mechanism for HS-PEE [54], [78]- [87]. An illustration of this mechanism is shown in Fig.…”

Section: ) Recent Advance In Hs-peementioning

confidence: 99%

Section: F Summary and Concluding Remarksmentioning

confidence: 99%

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Reversible data hiding: Advances in the past two decades

et al. 2016

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“…Much of the research focused on reversible data hiding is in pixel domain. They are based on lossless compression [ 2 , 3 , 4 , 5 , 6 ], difference expansion [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ], or histogram shifting [ 7 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. A recent advances [ 19 , 20 , 39 , 40 ] use two dimensional histogram shifting to achieve higher embedding capacity and lower distortion.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding in JPEG Images Using Quantized DC

Kim

Huang

Kim

2019

Entropy

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Reversible data hiding in JPEG images has become an important topic due to the prevalence and overwhelming support of the JPEG image format these days. Much of the existing work focuses on embedding using AC (quantized alternating current coefficients) to maximize the embedding capacity while minimizing the distortion and the file size increase. Traditionally, DC (quantized direct current coefficients) are not used for embedding, due to the assumption that the embedding in DCs cause more distortion than embedding in ACs. However, for data analytic which extracts fine details as a feature, distortion in ACs is not acceptable, because they represent the fine details of the image. In this paper, we propose a novel reversible data hiding method which efficiently embeds in the DC. The propose method uses a novel DC prediction method to decrease the entropy of the prediction error histogram. The embedded image has higher PSNR, embedding capacity, and smaller file size increase. Furthermore, proposed method preserves all the fine details of the image.

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“…Further modifications are presented in Ref. Xuan et al 35 and Kuo et al 36 apply error control and histogram shifting to embed data keeping good stego-image quality. The median edge detector predictor is used by Thodi et al 24 by Hong et al 25 and by Yang et al 26 while the gradient adjusted prediction is exploited by Fallahpour et al 27 Kuribayashi et al 28 apply difference expansion and prediction, while Tsai et al 29 and Pan et al 30 apply predictive coding and histogram shifting in the specific domain of medical imaging.…”

Section: Introductionmentioning

confidence: 99%

Difference expansion and prediction for high bit-rate reversible data hiding

Boato

Carli

Battisti

et al. 2012

J. Electron. Imaging

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Reversible data hiding deals with the insertion of auxiliary information into a host data without causing any permanent degradation to the original signal. In this contribution a high capacity reversible data hiding scheme, based on the classical difference expansion insertion algorithm, is presented. The method exploits a prediction stage, followed by prediction errors modification, both in the spatial domain and in the S-transform domain. Such two step embedding allows us to achieve high embedding capacity while preserving a high image quality, as demonstrated in the experimental results.

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Double-threshold reversible data hiding

Cited by 13 publications

References 8 publications

Reversible data hiding: Advances in the past two decades

Reversible data hiding: Advances in the past two decades

Reversible Data Hiding in JPEG Images Using Quantized DC

Difference expansion and prediction for high bit-rate reversible data hiding

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