No abstract

In this paper, the proposed work is about to store the patient information in the medical image itself that can be a CT scan or the MRI image. Medical records are extremely sensitive patient information and require uncompromising security during both storage and transmission. In the traditional way, the patient information and patient test reports are kept in different tables or databases or locations. But, this kind of data management can have some human oriented errors such as transfer of wrong report to a patient. Errors can be prevented by hiding the data in scan report itself. It will improve the reliability of the medical information system. In our paper the work is divided in two main stages, first to identify the ROI and RONI of the image. Here, the ROI is defined in terms of information part of medical image and RONI is defined in terms of non-information part of the MRI image. It will avoid the user to destroy the valuable information from the image. Watermark is encrypted by using RSA. The second stage is about to hide the image in RONI. A DWT based approach is used to hide such information. General TermsTo provide a two-way security to the medical images by using DWT and RSA along with the preservation of ROI.

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.

We propose a hybrid grasshopper optimizer to reduce the size of the feature set in the steganalysis process using information theory and other stochastic optimization techniques. This paper results from the stagnancy of local minima and slow convergence rate by the grasshopper algorithm in optimization problems. Therefore, we enhance the grasshopper optimization (GOA) performance with chaotic maps to make it Chaotic GOA (CGOA). Then, we combine the CGOA with adaptive particle swarm optimization (APSO) to make it Chaotic Particle-Swarm Grasshopper Optimization Algorithm (CPGOA). Next, we use the proposed optimizer with entropy to find the best feature subset of the original Subtractive Pixel Adjacency Model (SPAM) and Spatial Rich Model (SRM) feature set. Finally, the proposed technique is experimented with to detect the spatial domain steganography with different embedding rates on the BOSSbase 1.01 grayscale image database. The results show the improved results from the proposed hybrid optimizer compared to the original GOA and other state-of-the-art feature selection methods in steganalysis.

Steganography is the art of secretly transferring of data and steganalysis is the art of detecting that hidden data embedded in cover media. In the past years many powerful and robust methods of steganography and steganalysis have been reported in the literature. In this present work, a Steganalysis technique for Histogram-Shifting Based Data Hiding is designed to detect hidden data by using spike generation and template matching. The proposed work analyzes the characteristics of histogram changes during data hiding procedure, and then uses these features to distinguish between stego and original image.The presented work perform the steganalysis in four steps: First, an input image is filtered by using perwitt operator for edge detection. Second, the spike image is divided into 8x8 blocks and then histogram is generated for each block. Third, histogram of each block of stego-image and original image is compared by using 5 similarity measures (norm distance, cosine distance, Euclidean distance, Chi-squared distance, Entropy distance). Fourth, Neural Network (NN) is trained as a classifier to discriminate stego image from original image. Experimental results indicate that the proposed steganalysis method is better than the method proposed by Der-Chyuan Lou et. al. [1] and can effectively detect stego image at low bit rates.