In stenography, embedding data within an image has a trade-off between image quality and embedding capacity. Specifically, the more data are concealed within a carrier image, the further distortion the image suffers, causing a decline in the resultant stego image quality. Embedding high capacity of data into an image while preserving the quality of the carrier image can be seen as an optimization problem. In this paper, we propose a novel spatial steganography scheme using genetic algorithms (GAs). Our scheme utilizes new operations to increase least significant bits (LSB) matching between the carrier and the stego image which results in increased embedding capacity and reduced distortion. These operations are optimized pixel scanning in vertical and horizontal directions, circular shifting, flipping secret bits and secret data transposing. We formulate a general GA-based steganography model to search for the optimum solutions. Finally, we use LSB substitution for data embedding. We conduct extensive experimental testing of the proposed scheme and compare it to the state-of-art steganography schemes. The proposed scheme outperforms the relevant GA-based steganography methodologies.

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An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms

The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient’s information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an Adaptive Genetic Algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The proposed model can also prevent attacks, such as steganalysis or RS attacks.

Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

Cloud computing along with the Internet of Things (IoT) is proving to be an essential tool for delivering better healthcare services. However, maintenance, privacy, confidentiality, and security of Electronic Health Information (EHI) pose a huge challenge in telemedicine. The sharing of EHI with a remote doctor over the cloud is an important issue since the minute variation may lead to the wrong diagnosis. Despite the plethora of research in this field, there is an immense necessity to develop the algorithms for enhancing security in e-healthcare systems. In this paper, an innovative Reversible Data Hiding (RDH) scheme using Lagrange’s interpolation polynomial, secret sharing, and bit substitution for EHI security has been proposed. The cover medical image is sub-sampled, into four shares. Image interpolation is used to enlarge the subsamples, for hiding EHI. The secret information is processed using Lagrange’s interpolation polynomial before being embedded in the various cover image shares. The data is embedded into the interpolated sub-sampled shares at the locations pre-defined by the algorithm. The distributive nature of embedded data enhances the security of the proposed framework while maintaining reversibility. We show that only 75% of shares are required to obtain the whole embedded data and the undistorted cover image. The proposed scheme outperforms the schemes under comparison in terms of imperceptibility and payload. It can reversibly embed 163,840 bits (0.75 bits per pixel) with an average PSNR of about 52.38 dB. The average values of relative entropy, the difference in relative entropy, standard deviation, and cross-correlation are 7.3242, 0.0382, 65.0539, and 0.9838, respectively, for the first sub-sample. It shows an increase of about 3 dB for a payload of 1, 30,000 bits when compared to the state-of-art. Further, it is pertinent to mention that the proposed scheme has lower computational complexity and is hence useful for e-healthcare applications. Given all the attributes of the scheme along with its lower computational complexity, it is suitable for EHI security in a distributive environment like cloud computing.

Reversible Data Hiding for Electronic Patient Information Security for Telemedicine Applications

This work's primary goal is to secure the transmission of text hidden within the cover image using steganography over a public network of computers. Steganography is a powerful tool for concealing information within a cover image so that the concealed message remains undetectable. As a result, steganography refers to concealed writing. The secure transmission of information over a public network communication channel using steganography occurs in two stages, the first on the sender side and the second on the receiver side. In the first phase, steganography is normally applied to conceal the encrypted information within the image as a cover. The encrypted data is implanted inside the cover image using an improved least significant bit steganography method. The secret key obtained by the embedding algorithm is shared with the message retrieval algorithm on the receiver side to retrieve the message. Finally, the embedded text message is identified using a hybrid convolution regression adaptive integrated neural network (CRAINN) approach.

Secure Text Extraction From Complex Degraded Images by Applying Steganography and Deep Learning

In this letter, we present a reversible data hiding scheme based on histogram modification. We exploit a binary tree structure to solve the problem of communicating pairs of peak points. Distribution of pixel differences is used to achieve large hiding capacity while keeping the distortion low. We also adopt a histogram shifting technique to prevent overflow and underflow. Performance comparisons with other existing schemes are provided to demonstrate the superiority of the proposed scheme.

Reversible Data Hiding Based on Histogram Modification of Pixel Differences

The recent advancement in computing technologies and resulting vision based applications has given rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation technique for message embedding in the medical cover images. In addition to, ensure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm for optimal pixel adjustment process that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.

https://link.springer.com/content/pdf/10.1007/s11045-018-0575-3.pdf

An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

The recent advancement in computing technologies and resulting vision based applications have gives rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to get manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation (DRT) technique for message embedding in the medical cover images. In addition, to assure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm (AGA) for optimal pixel adjustment process (OPAP) that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.

An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications

In this paper, a robust and enhanced evolutionary computing assisted Takagi Sugeno (T–S) fuzzy controller was developed for automotive fuel injection control. To augment the rule generation and fuzzy parameter estimation, we propose an enhanced evolutionary computing algorithm named Hierarchical Adaptive Genetic Algorithm (HAGA). The proposed HAGA model was applied to augment T–S fuzzy controller that in conjunction with Fuzzy Clustering Mean (FCM) has enabled optimal rule generation and control parameter estimation. The proposed HAGA model exploits Hierarchical concept-based AGA implementation that itself embodies novelties like adaptive crossover and mutation probability, to enable accurate, swift, and efficient control function by T–S Fuzzy controller. These novelties strengthen the proposed HAGA-TS fuzzy controller to exhibit time efficient and accurate control function that could be of utmost significance for non-linear process control. The proposed controller design is examined for its efficacy over automotive or vehicle data containing fuel injection rate, throttle angle, emission products etc., where considering current emission and/or torque requirements, the throttle angle is varied to achieve environmentally friendly and cost-efficient vehicle design. The simulation results revealed that the proposed HAGA TS-fuzzy controller outperforms other state of art evolutionary computing-based approaches such as GA, PSO based T–S fuzzy controller.

Hierarchical Adaptive Genetic Algorithm Based T–S Fuzzy Controller For Non-linear Automotive Applications

Intelligent data science enabled reactive power optimization of a distribution system

Exploring the significance of geo-tagged information of a satellite image and associated shadow information in the image to enable man-made infrastructure dimensional characterization, in this paper a novel approach has been developed to estimate the height of a man-made structure accurately. Our proposed method applies shadow detection and shadow feature along with geo-tagged information retrieved through Google Earth tools, to estimate the structural dimension of the man-made infrastructure. To characterize the dimensional features of the man-made structure, we have considered Google Earth satellite images with precise and crisp edge as well as shadow length information, which have been obtained using freely available Google Earth information retrieval tool. Applying our proposed mathematical formulation, the height of man-made structures as well as neighbouring structures have been estimated which can be used for certain strategic decision purposes. The proposed method can be applicable for professionals exploring dimensional characterization of relatively low height constructions as well as defence purposes. Our proposed model has outperformed majority of existing approaches, particularly in terms of accuracy of height estimation of man-made structures.

Elsaid Md. Abdelrahim

Papers

An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications

Hierarchical Adaptive Genetic Algorithm Based T–S Fuzzy Controller For Non-linear Automotive Applications

Intelligent data science enabled reactive power optimization of a distribution system

Shadow Detection and Geo-tagged Image Information Based Strategic Infrastructure Characterization