A Blind High-Capacity Wavelet-Based Steganography Technique for Hiding Images into other Images

Hamad, Safwat; Khalifa, Amal; Elhadad, Ahmed

doi:10.4316/aece.2014.02006

Cited by 10 publications

(7 citation statements)

References 11 publications

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“…(1) Function [EW_img] � Extraction (W_3D_Obj, β, Key 1 , Key 2 , Key 3 ) (2) //W_3D_Obj : Watermarked 3D object vertices, Key 1 , Key 2 and Key 3 : Encryption keys (3) Normalized W_3D_Obj � Normalize(W_3D_Obj) (4) [Vertex x, Vertex y, Vertex z] � Preprocessing(Normalized W_3D_Obj) (5) for j � 1 to length(Msg vector) do (6) [Coefficient 1 , Coefficient 2 , Coefficient 3 ] � DC T(Vertex x(j), Vertex y(j), Vertex z(j)) (7) Extract the RecEncMsg value within the Coefficients values as the following equation system:…”

Section: Methodsmentioning

confidence: 99%

“…In modern times, watermarking techniques for a wide range of digital media were utilized as a host cover to hide or embed a piece of information message in such a way that it is imperceptible to a human observer. Usually, the digital media covers can take any form such as images [1][2][3][4][5][6][7], videos [8][9][10][11][12], audio [13][14][15], and DNA sequences [16,17]. Even so the 3D objects are widely available and important, there are a few existing watermarking techniques.…”

Section: Introductionmentioning

confidence: 99%

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A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

Al-Saadi

Ghareeb

Elhadad

2021

Computational Intelligence and Neuroscience

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In this paper, we propose a novel model for 3D object watermarking. The proposed method is based on the properties of the discrete cosine transform (DCT) of the 3D object vertices to embed a secret grayscale image three times. The watermarking process takes place by using the vertices coefficients and the encrypted image pixels. Moreover, the extraction process is totally blind based on the reverse steps of the embedding process to recover the secret grayscale image. Various performance aspects of the method are measured and compared between the original 3D object and the watermarked one using Euclidean distance, Manhattan distance, cosine distance, and correlation distance. The obtained results show that the proposed model provides better performances in terms of execution time and invisibility.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

Al-Saadi

Ghareeb

Elhadad

2021

Computational Intelligence and Neuroscience

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“…[19]; on the other hand, the vectors of the ranges in Figure 7e are 𝒂 = [𝑎 , 𝑎 , 𝑎 ] = [7,11,12] and 𝒃 = [𝑏 , 𝑏 , 𝑏 ] = [3,9,19].…”

Section: Optimization Processmentioning

confidence: 99%

“…We can identify three basic types of RDH approaches [1], including lossless compression (LC) [2][3][4], difference expansion (DE) [5][6][7][8], and histogram shifting (HS) [9][10][11][12][13][14][15]. Previous methods have been combined to substantially improve results [8,14,[16][17][18][19][20]. In the context of HS schemes, the first HS technique described in [9] selects a range in the histogram between a peak-bin and a zero bin, and then, the bins in the open interval are shifted toward the zero bin to create a vacancy.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding with a New Local Contrast Enhancement Approach

et al. 2022

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Reversible data hiding schemes hide information into a digital image and simultaneously increase its contrast. The improvements of the different approaches aim to increase the capacity, contrast, and quality of the image. However, recent proposals contrast the image globally and lose local details since they use two common methodologies that may not contribute to obtaining better results. Firstly, to generate vacancies for hiding information, most schemes start with a preprocessing applied to the histogram that may introduce visual distortions and set the maximum hiding rate in advance. Secondly, just a few hiding ranges are selected in the histogram, which means that just limited contrast and capacity may be achieved. To solve these problems, in this paper, a novel approach without preprocessing performs an automatic selection of multiple hiding ranges into the histograms. The selection stage is based on an optimization process, and the iterative-based algorithm increases capacity at embedding execution. Results show that quality and capacity values overcome previous approaches. Additionally, visual results show how greyscale values are better differentiated in the image, revealing details globally and locally.

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“…The Fourier transform (FT) is a transform function that is used to decompose a function into waves of different amplitudes and frequencies [9]. The Discrete Cosine transform (DCT) [10] and The Discrete Wavelet Transform (DWT) [11] are other transform functions that are typically utilized for image steganography. In fact, WLT decomposes a function into a series of wavelets that are wave-like oscillations which are localized in time [12].…”

Section: Introductionmentioning

confidence: 99%

Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques

Khalifa

Guzman

2022

Symmetry

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Digital image steganography is the process of embedding information within a cover image in a secure, imperceptible, and recoverable way. This research extends a symmetry-adapted deep-learning approach to identify hidden patterns of images using two-dimensional convolutional neural networks (CNN). The proposed method (SteGuz) uses three CNNs to implement the different phases of the steganography process on digital image covers. SteGuz introduced a gain function, based on several image similarity metrics, to maximize the imperceptibility of the hiding process. Using 10 different pairs of cover-secret images, the performance of the proposed method was measured in terms of standard metrics such as peak signal to noise ratio (PSNR) and structured similarity index measurement (SSIM). The results showed that the proposed methodology outperformed the original method in terms of both imperceptibility and recoverability. In addition, when compared with some existing methods, SteGuz proved the outstanding performance of achieving a very high PSNR value while maintaining high accuracy of the extracted image.

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A Blind High-Capacity Wavelet-Based Steganography Technique for Hiding Images into other Images

Cited by 10 publications

References 11 publications

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

Reversible Data Hiding with a New Local Contrast Enhancement Approach

Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques

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