Medical JPEG image steganography based on preserving inter-block dependencies

Liao, Xin; Yin, Jiaojiao; Guo, Sujing; Li, Xiong; Sangaiah, Arun Kumar

doi:10.1016/j.compeleceng.2017.08.020

Cited by 135 publications

(53 citation statements)

References 14 publications

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“…Though light is less than 5 K lux in Figure 1, some white bright spots still appear in the middle of the image because the light source is located close to the lesion area and the shooting angle is not well selected. 8 This will bring great errors to recognition of medical image.…”

Section: Affected Factors Of Medical Image Qualitymentioning

confidence: 99%

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Wang

Cui

2018

Concurrency and Computation

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Most current medical images are collected in the presence of interference by ignored interferences such as illumination, occlusion, etc. The recognition rate is low for multi-resolution images in the case of color distortion. Therefore, a novel fuzzy clustering recognition algorithm of multi-resolution medical image was proposed in this paper. First, medical images were analyzed from both acquisition time, shooting angle, resolution, natural light measurement, and background. Secondly, in order to avoid partial occlusion, region was recalculated by correlation between Fourier and Merlin transform. Moreover, Euclidean distance between samples were identified by standardized eigenvalues, where smallest distance obtained maximum degree of membership. Experiment results showed that the proposed method had higher recognition rate (accuracy 90.46% and sensitiveness 97.89%) and stronger anti-interference than current methods. KEYWORDS fuzzy clustering, image processing, image recognition, medical image, multi-resolution 1 INTRODUCTION Today, medical image recognition is based on features of images with pattern recognition method. 1 Medical image recognition is the core content of auto-diagnosis of medical images, which is a hotspot in medical research domain. 2 Medical images contain rich features and rules of humans, which are images with high resolution, massive amount, and complex features. Research of medical image recognition has always faced challenges recently. Research and exploration of suitable algorithms for automatic recognition of medical images have important and practical significance for both theoretical and practical study. It is of great practical value to assist doctors in the clinical diagnosis by medical images. 3 In order to analyze and classify images by using intelligent algorithms, computers can be used to process medical images and extract image features such as depth information by self-learning technology. These analysis and classification improve the accuracy of medical image recognition and assist doctors in diagnosis.An image recognition method combined multiple features to achieve image classification by collecting visual information such as image color, shape, and texture. 4 Recently, there were many algorithms for medical image recognition with multi-resolution. Though some research results had been reached a good result in some.Moeskops et al proposed a medical image recognition algorithm based on multiple modalities. 5 By using cameras to obtain medical information images, this method is easy to implement, but its recognition accuracy is very low because in the analysis of the patient's medical image, there will be some areas of image features overlap, occlusion and so on. Rebouças Filho et al proposed an automatic histologically-closer classification of skin lesions. The R, G, and B components of the collected medical image are converted into H, I, and S color models for description. The use of color features to identify, simple operation, high real-time, are not sensitive to image o...

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Section: Affected Factors Of Medical Image Qualitymentioning

confidence: 99%

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Wang

Cui

2018

Concurrency and Computation

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“…In frequency domain based data hiding techniques, the original image is first transformed into frequency coefficients and then the secret data is embedded as part of transformed frequency coefficients. In compression domain based data hiding techniques, the original image is first compressed into compressed codes using any available image compression methods such as Joint Photographic Experts Group (JPEG) [13], Vector Quantization (VQ) [8], Graphics Interchange Format (GIF) [14], and Block Truncation Coding (BTC) [15] and then the secret data is embedded as part of the compressed codes. The secret data that is hidden as part of the compressed image is not easily visible and hackers or attackers will not usually be wary on stego-images.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding Technique using AMBTC based Bitmap Manipulation

Uma¹,

Vimala²

2020

IJCA

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In this research work, it has been proposed to use images to hide the secret data as it is difficult to extract secret data from the image. Using images to hide and transmit secret data, leads to additional overhead of increased cost associated with storage and transmission cost, as the image requires more space for storage. However, hiding data inevitably destroys the host image, even though the distortion is imperceptible. To overcome such drawbacks, image compression techniques are used for both, hiding the secret data and to reduce the storage and transmission cost. To enhance the hiding capacity and maintain the quality of the host image after embedding hidden data, we present a high payload reversible data hiding scheme that is based on the Absolute Moment Block Truncation Coding (AMBTC) compression domain. We exploit the feature of inter block redundancy in an AMBTC compressed image to improve the coding efficiency of compressed images. Normally, the AMBTC technique transforms an input image into a set of blocks and each block in turn is transformed into a set of a bit-plane and two quantizers. But in the proposed method, these blocks are categorized into Shade and Edge blocks based on the magnitude of amplitude values. For a Shade block, two values are stored, one being the block mean and the other value being the secret data. Two bytes of secret data is stored creating an illusion that the bitplane is stored. For Edge blocks, high mean and low mean are stored as two quantizers and a respective Bitplane is generated. The shade block helps in embedding 3 bytes of secret data leading to higher embedding capacity and increases the complexity of identifying the existence of secret data. It also compresses the stego-image without much degradation in the visibility of stego image.

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“…Network multimedia comes into fashion in the form of services; there are many methods to protect multimedia data in traditional service mode, such as steganography [1,2] and data embedding [3]. By providing diversified media services, a new service mode, multimedia computing, has become an attractive technology to generate, edit, process, and search various media contents, like images, videos, audios, graphs, and so on [4].…”

Section: Introductionmentioning

confidence: 99%

“…It can guarantee the security and correctness of the data. (2) We study and implement a privacy-preserving set intersection protocol based on GM homomorphic encryption scheme and Bloom filter, and the proposed protocol is proved to be significant. (3) In detail, the protocol has some characteristics as follows:…”

Section: Introductionmentioning

confidence: 99%

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

Zhu

Chen

Sun

et al. 2018

Security and Communication Networks

Self Cite

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With the development of cloud computing, the advantages of low cost and high computation ability meet the demands of complicated computation of multimedia processing. Outsourcing computation of cloud could enable users with limited computing resources to store and process distributed multimedia application data without installing multimedia application software in local computer terminals, but the main problem is how to protect the security of user data in untrusted public cloud services. In recent years, the privacy-preserving outsourcing computation is one of the most common methods to solve the security problems of cloud computing. However, the existing computation cannot meet the needs for the large number of nodes and the dynamic topologies. In this paper, we introduce a novel privacy-preserving outsourcing computation method which combines GM homomorphic encryption scheme and Bloom filter together to solve this problem and propose a new privacy-preserving outsourcing set intersection computation protocol. Results show that the new protocol resolves the privacy-preserving outsourcing set intersection computation problem without increasing the complexity and the false positive probability. Besides, the number of participants, the size of input secret sets, and the online time of participants are not limited.

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Medical JPEG image steganography based on preserving inter-block dependencies

Cited by 135 publications

References 14 publications

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Reversible Data Hiding Technique using AMBTC based Bitmap Manipulation

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

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