Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing

Al-Dhamari, Ahlam; Darabkh, Khalid A.

doi:10.4236/jsea.2017.101004

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…In addition, it may be a decent expansion to build up a methodology that considers the half breed area. So there are tentative arrangements to create modulus work based plans for other media, for example, sounds and video [29].…”

Section: Discussionmentioning

confidence: 99%

The Investigation of LSB based Image Steganographic Techniques in Spatial Domain for Secure Communication

Rahman

Masood

Khan

et al. 2019

SJET

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Confidentiality, integrity and authenticity for secure data are required for all the conveying bodies. Distinctive methodologies are utilized to adopt the security issues like digital certificate, digital signature and cryptography. These techniques alone cannot be traded off. Steganography is one of the answers to the security as it hides the secret information. Steganography has the importance in view of its exponential advancement and riddle data of possible PC customers over the web. It can likewise be depicted as the examination of indistinct information that usually manages the methodologies for concealing the closeness of the passed-on message. Mostly part of data covering is refined in correspondence to image, content, voice or sight and sound substance for copyright, military correspondence, confirmation and various diverse purposes. In this paper Least Significant Bit (LSB) technique for image steganography has been observed under various cases. Results for different scenarios have been looked at and the condition of the science assessment and examination of various accessible strategies for steganography have been given. The principles pursued are equivalent to give into the writing. Normal models and procedures drawn from the literature are utilized to obtain the results. This survey concludes with recommendations and supporters for the caries-oriented mechanism.

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Section: Discussionmentioning

confidence: 99%

The Investigation of LSB based Image Steganographic Techniques in Spatial Domain for Secure Communication

Rahman

Masood

Khan

et al. 2019

SJET

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“…Mathieu et al [44] argued that the PSNR is a better metric for assessing quality. The PSNR is defined as follows [45], [46]:…”

Section: A Evaluation Metricsmentioning

confidence: 99%

U-ASD Net: Supervised Crowd Counting Based on Semantic Segmentation and Adaptive Scenario Discovery

Hafeezallah

Al-Dhamari²,

Abu-Bakar

2021

IEEE Access

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Crowd counting considers one of the most significant and challenging issues in computer vision and deep learning communities, whose applications are being utilized for various tasks. While this issue is well studied, it remains an open challenge to manage perspective distortions and scale variations. How well these problems are resolved has a huge impact on predicting a high-quality crowd density map. In this study, a hybrid and modified deep neural network (U-ASD Net), based on U-Net and adaptive scenario discovery (ASD), is proposed to get precise and effective crowd counting. The U part is produced by replacing the nearest upsampling in the encoder of U-Net with max-unpooling. This modification provides a better crowd counting performance by capturing more spatial information. The max-unpooling layers upsample the feature maps based on the max locations held from the downsampling process. The ASD part is constructed with three light pathways, two of which have been learned to reflect various densities of the crowd and define the appropriate geometric configuration employing various sizes of the receptive field. The third pathway is an adaptation path, which implicitly discovers and models complex scenarios to recalibrate pathway-wise responses adaptively. ASD has no additional branches to avoid increasing the complexity. The designed model is end-to-end trainable. This integration provides an effective model to count crowds in both dense and sparse datasets. It also predicts an elevated quality density map with a high structural similarity index and a high peak signal-to-noise ratio. Several comprehensive experiments on four popular datasets for crowd counting have been carried out to demonstrate the proposed method's promising performance compared to other state-of-the-art approaches. The proposed model achieves the lowest count error in terms of the MAE in ShanghaiTech Part A, Part B, and Mall datasets with 64.6, 7.5, and 1.8, respectively. Moreover, it achieves the lowest count error in terms of the MSE in ShanghaiTech Part B, UCF CC 50, UCSD, and Mall datasets with 12.4, 217.8, 2.1, 2.2, respectively. In addition, the proposed model accomplishes the best quality density maps on all the utilized datasets. Furthermore, a new dataset with its manual annotations, called Haramain with three different scenes and different densities, is introduced and used for evaluating the U-ASD Net.

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“…It embeds data in the three directions within a 2 × 2 block. Later, Dhamari and Darabkh [5] have developed a steganographic algorithm applying PVD and modulus function (MF-PVD) for enhancing the security of the scheme. An efficient data hiding technique is presented in the scheme [19] that mixes PVD, MF, and particle swarm optimization (PSO) methods for attaining the high quality of the analyzed images.…”

Section: Introductionmentioning

confidence: 99%

High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

Mandal

Mukherjee

2021

Multimed Tools Appl

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Data hiding is an important research topic over the years. The PVD based techniques embed secret data into the cover image on the difference between the two neighboring pixels. Most of the PVD based techniques are unidirectional that signifies less embedding capacity. The embedding capacity of the PVD technique is utilized in this article to embed the data in multi-directions. To improve the embedding capacity, a new high capacity data hiding technique based on decreased difference expansion (DDE) method and the multidirectional pixel value differencing (MPVD) is proposed. It is based on the block embedding technique for embedding data in the non-sequential locations. The cover image is divided into 2 × 2 non-overlapping blocks. A basic pixel is selected and data hiding takes place in three pixel pairs containing the basic pixel. Distortion due to message embedding is high in the difference expansion (DE) based data hiding techniques. DDE method improves the DE method by reducing the difference between two pixels with a logarithm function and hence increases the quality of the images compared to the DE method. The proposed technique achieves an average embedding capacity of 2.88 bpp which is higher than several recent state-of-the-art works. It withstands the tests of StirMark Benchmark 4.0, and the steganalysis attacks.

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Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing

Cited by 24 publications

References 49 publications

The Investigation of LSB based Image Steganographic Techniques in Spatial Domain for Secure Communication

The Investigation of LSB based Image Steganographic Techniques in Spatial Domain for Secure Communication

U-ASD Net: Supervised Crowd Counting Based on Semantic Segmentation and Adaptive Scenario Discovery

High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

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