Overlapping pixel value ordering predictor for high-capacity reversible data hiding

Lee, Chin‐Feng; Shen, Jau-Ji; Kao, Yu-Chi; Agrawal, Somya

doi:10.1007/s11554-019-00872-z

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…c is referred to as one of the cover images C, which can be represented by the probability distribution function P. We made the cover images C follow with P and a secret message M is embedded, and the generated steganographic images S also follow the probability distribution function Q. The statistical detection ability can be quantified by the KL divergence shown in formula (7) or the JS divergence in formula (8),…”

Section: The Objective Fuctionmentioning

confidence: 99%

“…To solve this problem, researchers proposed a new information hiding method-coverless steganography-in 2015. Compared with the traditional approaches, which need to adopt the specified cover image for embedding the secret data, such as Highly Undetectable SteGO (HUGO) and JPEG compression [4][5][6][7], the coverless steganography no longer modifies the cover images, which is why it is called coverless. It is achieved by means of mapping with secret information.…”

Section: Introductionmentioning

confidence: 99%

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Coverless Image Steganography Based on Generative Adversarial Network

et al. 2020

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Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

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Section: The Objective Fuctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coverless Image Steganography Based on Generative Adversarial Network

et al. 2020

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“…However, there was still ample room for improvement even though the obtained errors were efficiently modified. Interestingly, many scholars were attracted to this method due to its low embedding capacity [19][20][21]. This led to another method, Improved PVO (IPVO) [22] proposed by Peng and Yang in 2014, which was an improvement to the PVO method.…”

Section: Introductionmentioning

confidence: 99%

PVO-Based Reversible Data Hiding Exploiting Two-Layer Embedding for Enhancing Image Fidelity

Lee

Shen

et al. 2020

Symmetry

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Recently, Li et al. proposed a data hiding method based on pixel value ordering (PVO) and prediction error expansion (PEE). In their method, maximum and minimum values were predicted and the pixel values were modified to embed secret data. Thereafter, many scholars have proposed improvisations to the original PVO method. In this paper, a Reversible data hiding (RDH) method is proposed where the secret data is dispersed into two layers using different modes of operations. The second layer changes the dividing mode, and the first and the second layers do not take duplicate blocks. Under a fixed embedding capacity, threshold value and block size are controlled, complex blocks are filtered and the secret data is hidden in smooth blocks. This paper also compares the effectiveness of four well-known PVO series methods, the latest PVO methods, difference expansion (DE) method and reduced difference expansion (RDE) method. Experimental results show that the proposed method reduces distortion in the image, thereby enhancing the visual symmetry/quality compared to previous state-of-the-art methods and increasing its high application value.

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“…They proposed a sparse recovery based predictor to improve the low accuracy of existing predictors, built the concentrated PEH to obtain good embedding performance, designed a new embedding strategy based on just noticeable difference (JND), and utilized the PEE technique to embed data. A series of methods based on pixel value ordering (PVO) [11], [12] have proposed to produce camousflaged pixels of good image quality combined with PEE. Ou et al [13] proposed a novel RDH framework based on the so called pairwise PEE, and used the pairwise PEE to embed data in a 2D PEH.…”

Section: Introductionmentioning

confidence: 99%

A Reversible Steganography Method With Statistical Features Maintained Based on the Difference Value

et al. 2020

IEEE Access

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Reversible data hiding (RDH) is a technique that slightly alters digital media (e.g. images or videos) to embed secret messages while the original digital media can be completely recovered without any error after the hidden messages have been extracted. In the past more than one decade, hundreds of RDH algorithms have been reported, and among these algorithms, the difference histogram shifting (DHS) based methods have attracted much attention. With DHS-based RDH, high capacity and low distortion can be achieved efficiently. But there occurs one problem that, with DHS, the difference values to embed secret bits are explored, and the other difference values are shifted to create vacant spaces, it will cause the difference value histogram changing significantly and draw the attention of steganalyzers. So, this paper proposed a new idea for RDH based on the difference value and with statistical features maintained (SFM) with simple implementation and high scalability, we embed the secret messages by keeping the difference values that need to be modified in the original range, and the other difference values would not be shifted. In addition, we need the original difference values as the key to extract the secret messages. In order to expand the embedding capacity further, we designed two algorithms that embed message in two different difference values and four different difference values, and these two methods are named SFM_A and SFM_B respectively. SFM_B can support greater amount of embedded message than SFM_A, but brings greater changes to the original image, which could lead to the decline of PSNR and SSIM. The experimental results show that through our method, the histogram of difference values is well maintained, and the degree of distortion of the image is improved at the same time.

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Overlapping pixel value ordering predictor for high-capacity reversible data hiding

Cited by 11 publications

References 34 publications

Coverless Image Steganography Based on Generative Adversarial Network

Coverless Image Steganography Based on Generative Adversarial Network

PVO-Based Reversible Data Hiding Exploiting Two-Layer Embedding for Enhancing Image Fidelity

A Reversible Steganography Method With Statistical Features Maintained Based on the Difference Value

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