Is image steganography natural?

Improved diagonal queue medical image steganography using Chaos theory, LFSR, and Rabin cryptosystem

In this article, we have proposed an improved diagonal queue medical image steganography for patient secret medical data transmission using chaotic standard map, linear feedback shift register, and Rabin cryptosystem, for improvement of previous technique (Jain and Lenka in Springer Brain Inform 3:39–51, 2016). The proposed algorithm comprises four stages, generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map), permutation and XORing using pseudo-random sequences, encryption using Rabin cryptosystem, and steganography using the improved diagonal queues. Security analysis has been carried out. Performance analysis is observed using MSE, PSNR, maximum embedding capacity, as well as by histogram analysis between various Brain disease stego and cover images.

Diagonal queue medical image steganography with Rabin cryptosystem

The main purpose of this work is to provide a novel and efficient method to the image steganography area of research in the field of biomedical, so that the security can be given to the very precious and confidential sensitive data of the patient and at the same time with the implication of the highly reliable algorithms will explode the high security to the precious brain information from the intruders. The patient information such as patient medical records with personal identification information of patients can be stored in both storage and transmission. This paper describes a novel methodology for hiding medical records like HIV reports, baby girl fetus, and patient’s identity information inside their Brain disease medical image files viz. scan image or MRI image using the notion of obscurity with respect to a diagonal queue least significant bit substitution. Data structure queue plays a dynamic role in resource sharing between multiple communication parties and when secret medical data are transferred asynchronously (secret medical data not necessarily received at the same rate they were sent). Rabin cryptosystem is used for secret medical data writing, since it is computationally secure against a chosen-plaintext attack and shows the difficulty of integer factoring. The outcome of the cryptosystem is organized in various blocks and equally distributed sub-blocks. In steganography process, various Brain disease cover images are organized into various blocks of diagonal queues. The secret cipher blocks and sub-blocks are assigned dynamically to selected diagonal queues for embedding. The receiver gets four values of medical data plaintext corresponding to one ciphertext, so only authorized receiver can identify the correct medical data. Performance analysis was conducted using MSE, PSNR, maximum embedding capacity as well as by histogram analysis between various Brain disease stego and cover images.

Locally optimal detection of image watermarks in the wavelet domain using Bessel K form distribution

A uniformly most powerful watermark detector, which applies the Bessel K form (BKF) probability density function to model the noise distribution was proposed by Bian and Liang. In this paper, we derive a locally optimum (LO) detector using the same noise model. Since the literature lacks thorough discussion on the performance of the BKF-LO nonlinearities, the performance of the proposed detector is discussed in detail. First, we prove that the test statistic of the proposed detector is asymptotically Gaussian and evaluate the actual performance of the proposed detector using the receiver operating characteristic (ROC). Then, the large sample performance of the proposed detector is evaluated using asymptotic relative efficiency (ARE) and "maximum ARE." The experimental results show that the proposed detector has a good performance with or without attacks in terms of its ROC curves, particularly when the watermark is weak. Therefore, the proposed method is suitable for wavelet domain watermark detection, particularly when the watermark is weak.