A Reversible Data Hiding Algorithm Based on Prediction Error With Large Amounts of Data Hiding in Spatial Domain

Li, Shuai; Hu, Liang; Sun, Chengyu; Chi, Ling; Li, Tuohang; Li, Hongtu

doi:10.1109/access.2020.3040048

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…In the context of the proposed method, 8-bit grayscale images were employed, and consequently, the MAX value used was 255. The PSNR equation can be expressed as Equation (19).…”

Section: Resultsmentioning

confidence: 99%

“…Sometimes, permanent distortion cannot be tolerated; therefore, many different reversible data-hiding schemes have been proposed. These data-hiding schemes are meant to extract information and recover the cover image in a lossless manner [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Some of these reversible data-hiding schemes (RDH) include difference expansion (DE); histogram shifting (HS) [6], from which the proposed method draws some ideas; prediction error (PE) [7,10,19], from which some ideas were drawn; and pixel value ordering (PVO) [13][14][15][16]20,21], which shall be compared with the proposed method in the results section of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…These data-hiding schemes are meant to extract information and recover the cover image in a lossless manner [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Some of these reversible data-hiding schemes (RDH) include difference expansion (DE); histogram shifting (HS) [6], from which the proposed method draws some ideas; prediction error (PE) [7,10,19], from which some ideas were drawn; and pixel value ordering (PVO) [13][14][15][16]20,21], which shall be compared with the proposed method in the results section of this paper. Reversible data hiding [15] involves embedding information into host data while maintaining the ability to fully recover both the original host data and the hidden information.…”

Section: Introductionmentioning

confidence: 99%

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High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

Weng,

Shongwe

et al. 2024

Electronics

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The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.

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“…In the context of the proposed method, 8-bit grayscale images were employed, and consequently, the MAX value used was 255. The PSNR equation can be expressed as Equation (19).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

Weng,

Shongwe

et al. 2024

Electronics

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“…RDH schemes are also explored for use with different image datasets, such as natural images [42], medical images [43], etc. Though many RDH schemes have been described in the literature [44,45], RDH techniques which operate in an encrypted medium [16,46] have become widely accepted due to their wide range of practical applications pertaining to the security of the cover medium.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform

et al. 2023

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Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of 0.0625 bits per pixel, outperforming many related RDH schemes with complete reversibility.

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“…However, the method has lower image quality under low hidden payload. In 2020, Li et al proposed an improved prediction-error expansion (I-PEE) scheme [25] to achieve higher embedding capacity and good image quality.…”

Section: Introductionmentioning

confidence: 99%

High-Capacity and High-Quality Reversible Data Hiding Method Using Recurrent Round-Trip Embedding Strategy in the Quotient Image

Lee

2021

Applied Sciences

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In previous research, scholars always think about how to improve the information hiding algorithm and strive to have the largest embedding capacity and better image quality, restoring the original image. This research mainly proposes a new robust and reversible information hiding method, recurrent robust reversible data hiding (triple-RDH), with a recurrent round-trip embedding strategy. We embed the secret message in a quotient image to increase the image robustness. The pixel value is split into two parts, HiSB and LoSB. A recurrent round-trip embedding strategy (referred to as double R-TES) is designed to adjust the predictor and the recursive parameter values, so the pixel value carrying the secret data bits can be first shifted to the right and then shifted to the left, resulting in pixel invariance, so the embedding capacity can be effectively increased repeatedly. Experimental results show that the proposed triple-RDH method can effectively increase the embedding capacity up to 310,732 bits and maintain a certain level of image quality. Compared with the existing pixel error expansion (PEE) methods, the triple-RDH method not only has a high capacity but also has robustness for image processing against unintentional attacks. It can also be used for capacity and image quality according to the needs of the application, performing adjustable embedding.

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A Reversible Data Hiding Algorithm Based on Prediction Error With Large Amounts of Data Hiding in Spatial Domain

Cited by 21 publications

References 34 publications

High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform

High-Capacity and High-Quality Reversible Data Hiding Method Using Recurrent Round-Trip Embedding Strategy in the Quotient Image

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