Difference expansion based reversible data embedding and edge detection

Gujjunoori, Sagar; Oruganti, Madhu

doi:10.1007/s11042-019-07767-y

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…The third LSB 2 is served as the natural marker bit and the embedding strategy is determined by it. More specifically, when 2 = 0, the surrounding pixels of this block are partitioned into (4,4) groups to embed secret data; when 2 = 1, they are partitioned into (2,2,2,2) groups. In addition, the Data 1, Data 2, and Data 3 are three different random streams.…”

Section: Adaptive Methods Based On 2dand4d Flexible Magic Matrices Without Marker Bitmentioning

confidence: 99%

“…The difference between them is that the reversible hiding scheme can reconstruct the original cover image after secret data is extracted. Many successful reversible data hiding schemes have been proposed [1][2][3][4][5][6][7], including the difference expansion [3,4], the prediction error expansion (PEE) [5,6], the histogram shifting [8,9], the neural network [10][11][12] and so on. For instance, the prediction error expansion is first proposed by Thodi et al [13], which embeds the secret information by expanding the prediction errors obtained from the difference of original and predicted value of the target pixel.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive data hiding scheme based on magic matrix of flexible dimension

Wu¹,

Horng²,

Chang³

2021

KSII TIIS

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Magic matrix-based data hiding schemes are applied to transmit secret information through open communication channels safely. With the development of various magic matrices, some higher dimensional magic matrices are proposed for improving the security level. However, with the limitation of computing resource and the requirement of real time processing, these higher dimensional magic matrix-based methods are not advantageous. Hence, a kind of data hiding scheme based on a single or a group of multi-dimensional flexible magic matrices is proposed in this paper, whose magic matrix can be expanded to higher dimensional ones with less computing resource. Furthermore, an adaptive mechanism is proposed to reduce the embedding distortion. Adapting to the secret data, the magic matrix with least distortion is chosen to embed the data and a marker bit is exploited to record the choice. Experimental results confirm that the proposed scheme hides data with high security and a better visual quality.

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Section: Adaptive Methods Based On 2dand4d Flexible Magic Matrices Without Marker Bitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive data hiding scheme based on magic matrix of flexible dimension

Wu¹,

Horng²,

Chang³

2021

KSII TIIS

View full text Add to dashboard Cite

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“…Instead, other data hiding schemes embed secret messaves into the spatial domain, such as least significant bit (LSB) [8], [31], [32], pixel value differencing (PVD) [33], [34], histogram shifting [35], [36], [37], expansion based [38], [39], multiple bit-planes [40], [41]. These methods have higher embedding rates than frequency-domain based methods, but are usually less robust and less resistant to steganalysis [42], [43], [44], [45], [46].…”

Section: A Data Hiding Of Low Embedding Ratesmentioning

confidence: 99%

Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding

Li¹,

Liu²,

Guo³

et al. 2023

Preprint

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Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing works mainly force on designing DNNs with higher embedding rates or fancy functionalities. In this paper, we want to answer some fundamental questions: how to increase and what determines the embedding rate of deep hiding. To this end, we first propose a novel Local Deep Hiding (LDH) scheme that significantly increases the embedding rate by hiding large secret images into small local regions of cover images. Our scheme consists of three DNNs: hiding, locating, and revealing. We use the hiding network to convert a secret image in a small imperceptible compact secret code that is embedded into a random local region of a cover image. The locating network assists the revealing process by identifying the position of secret codes in the stego image, while the revealing network recovers all full-size secret images from these identified local regions. Our LDH achieves an extremely high embedding rate, i.e., 16×24 bpp and exhibits superior robustness to common image distortions. We also conduct comprehensive experiments to evaluate our scheme under various system settings. We further quantitatively analyze the trade-off between the embedding rate and image quality with different image restoration algorithms.

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“…and b is called auxiliary information, among which a ∈ [1, 2n], the embedded y 1 and y 2 . e formula ( 2) is no longer satisfied [9].…”

Section: Information Embeddingmentioning

confidence: 99%

“…By adding a number b , y 1 = x 1 + b and y 2 = x 2 satisfy formula ( 2 ), where

21

, and b is called auxiliary information, among which a ∈ [1,2 n ], the embedded y 1 and y 2 . The formula ( 2 ) is no longer satisfied [ 9 ]. Like the general wavelet transform method, the wavelet low- and high-frequency coefficients obtained by the first level decomposition can be further decomposed into higher order.…”

Section: Wavelet Transform and Information Hiding Technology In Plaintext Domainmentioning

confidence: 99%

Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

Ding

Yun-bo

Zhang

et al. 2021

Contrast Media & Molecular Imaging

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Aiming at the shortcomings of the existing lossless digital watermarking algorithm based on frequency domain in reversibility and embedding capacity, this study proposes a lossless digital image watermarking algorithm based on fractional wavelet transform, which is used for large-capacity reversible information hiding of images. First, the image is transformed by LeGall5/3 fractional wavelet, and then, the watermark is embedded in the high-frequency subband by the histogram shift method. In order to obtain maximum embedding capacity and reduce image distortion, the methods of selecting embedding parameters and stopping parameters are proposed, respectively. At the same time, in order to prevent overflow and reduce additional information, a new method of generating position map is proposed. The experimental results show that Lena is the result of multilayer embedding based on the algorithm in this study. In order to better observe the distortion phenomenon and enlarge the image, the Lena test image is the watermark image obtained after two and three layers of embedding, and its embedding capacity can be 2.7 bpp. It is proved that wavelet transform is suitable for encrypted images to implement covert communication.

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Difference expansion based reversible data embedding and edge detection

Cited by 18 publications

References 17 publications

Adaptive data hiding scheme based on magic matrix of flexible dimension

Adaptive data hiding scheme based on magic matrix of flexible dimension

Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding

Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

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