Zahoor Jan

A Secure Method for Color Image Steganography using Gray-Level Modification and Multi-level Encryption

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.

iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

Membrane proteins execute significant roles in cellular processes of living organisms, ranging from cell signaling to cell adhesion. As a major part of a cell, the identification of membrane proteins and their functional types become a challenging job in the field of bioinformatics and proteomics from last few decades. Traditional experimental procedures are slightly applicable due to lack of recognized structures, enormous time and space. In this regard, the demand for fast, accurate and intelligent computational method is increased day by day. In this paper, a two-tier intelligent automated predictor has been developed called iMem-2LSAAC, which classifies protein sequence as membrane or non-membrane in first-tier (phase1) and in case of membrane the second-tier (phase2) identifies functional types of membrane protein. Quantitative attributes were extracted from protein sequences by applying three discrete features extraction schemes namely amino acid composition, pseudo amino acid composition and split amino acid composition (SAAC). Various learning algorithms were investigated by using jackknife test to select the best one for predictor. Experimental results exhibited that the highest predictive outcomes were yielded by SVM in conjunction with SAAC feature space on all examined datasets. The true classification rate of iMem-2LSAAC predictor is significantly higher than that of other state-of- the- art methods so far in the literature. Finally, it is expected that the proposed predictor will provide a solid framework for the development of pharmaceutical drug discovery and might be useful for researchers and academia.

CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method

Information hiding is an active area of research where secret information is embedded in innocent-looking carriers such as images and videos for hiding its existence while maintaining their visual quality. Researchers have presented various image steganographic techniques since the last decade, focusing on payload and image quality. However, there is a trade-off between these two metrics and keeping a better balance between them is still a challenging issue. In addition, the existing methods fail to achieve better security due to direct embedding of secret data inside images without encryption consideration, making data extraction relatively easy for adversaries. Therefore, in this work, we propose a secure image steganographic framework based on stego key-directed adaptive least significant bit (SKA-LSB) substitution method and multi-level cryptography. In the proposed scheme, stego key is encrypted using a two-level encryption algorithm (TLEA); secret data is encrypted using a multi-level encryption algorithm (MLEA), and the encrypted information is then embedded in the host image using an adaptive LSB substitution method, depending on secret key, red channel, MLEA, and sensitive contents. The quantitative and qualitative experimental results indicate that the proposed framework maintains a better balance between image quality and security, achieving a reasonable payload with relatively less computational complexity, which confirms its effectiveness compared to other state-of-the-art techniques.

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.

A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition

Retrieving similar images from a large dataset based on the image content has been a very active research area and is a very challenging task. Studies have shown that retrieving similar images based on their shape is a very effective method. For this purpose a large number of methods exist in literature. The combination of more than one feature has also been investigated for this purpose and has shown promising results. In this paper a fusion based shapes recognition method has been proposed. A set of local boundary based and region based features are derived from the labeled grid based representation of the shape and are combined with a few global shape features to produce a composite shape descriptor. This composite shape descriptor is then used in a weighted ranking algorithm to find similarities among shapes from a large dataset. The experimental analysis has shown that the proposed method is powerful enough to discriminate the geometrically similar shapes from the non-similar ones.

Facial appearance and texture feature-based robust facial expression recognition framework for sentiment knowledge discovery

Facial sentiment analysis has been an enthusiastic research area for the last two decades. A fair amount of work has been done by researchers in this field due to its utility in numerous applications such as facial expression driven knowledge discovery. However, developing an accurate and efficient facial expression recognition system is still a challenging problem. Although many efficient recognition systems have been introduced in the past, the recognition rate is not satisfactory in general due to inherent limitations including light, pose variations, noise, and occlusion. In this paper, a hybrid approach of facial expression based sentiment analysis has been presented combining local and global features. Feature extraction is performed fusing the histogram of oriented gradients (HOG) descriptor with the uniform local ternary pattern (U-LTP) descriptor. These features are extracted from the entire face image rather than from individual components of faces like eyes, nose, and mouth. The most suitable set of HOG parameters are selected after analyzing them experimentally along with the ULTP descriptor, boosting performance of the proposed technique over face images containing noise and occlusions. Face sentiments are analyzed classifying them into seven universal emotional expressions: Happy, Angry, Fear, Disgust, Sad, Surprise, and Neutral. Extracted features via HOG and ULTP are fused into a single feature vector and this feature vector is fed into a Multi-class Support Vector Machine classifier for emotion classification. Three types of experiments are conducted over three public facial image databases including JAFFE, MMI, and CK+ to evaluate the recognition rate of the proposed technique during experimental evaluation; recognition accuracy in percent, i.e., 95.71, 98.20, and 99.68 are achieved for JAFFE, MMI, and CK+, respectively.

A review on automated diagnosis of malaria parasite in microscopic blood smears images

Malaria is a life-threatening disease caused by parasite of genus plasmodium, which is transmitted through the bite of infected Anopheles. A rapid and accurate diagnosis of malaria is demanded for proper treatment on time. Mostly, conventional microscopy is followed for diagnosis of malaria in developing countries, where pathologist visually inspects the stained slide under light microscope. However, conventional microscopy has occasionally proved inefficient since it is time consuming and results are difficult to reproduce. Alternate techniques for malaria diagnosis based on computer vision were proposed by several researchers. The aim of this paper is to review, analyze, categorize and address the recent developments in the area of computer aided diagnosis of malaria parasite. Research efforts in quantification of malaria infection include normalization of images, segmentation followed by features extraction and classification, which were reviewed in detail in this paper. At the end of review, the existent challenges as well as possible research perspectives were discussed.

Analysis of interaction trace maps for active authentication on smart devices

The availability and affordability of handheld smart devices have made life easier by enabling us to do work on the go. Their widespread use brings with it concerns relating to data security and privacy. The rising demand to secure private and highly confidential data found on smart devices has motivated researchers to devise means for ensuring privacy and security at all times. This kind of continuous user authentication scheme would add an additional layer of much needed security to smart devices. In this context, touch screen interactions have recently been studied as an effective modality to perform active user authentication on mobile devices. In this paper, a visual analysis based active authentication framework has been presented. Considering the touch screen as a canvas, interaction trace maps are constructed as a result of user interactions within various applications. The user touch gestures are captured and represented as drawing strokes on the canvas. The behavioral and physiological characteristics of users are modeled as signatures by combining texture and shape features from the interaction trace maps. A two-step mechanism with support vector machines exploit this signature to perform active user authentication. Experiments conducted with various datasets show that the proposed framework compares favorably with other state-of-the-art methods.

Computer Aided System for Leukocytes Classification and Segmentation in Blood Smear Images

Detection and counting of white blood cells (WBC) in blood samples provides valuable information to medical specialists, helping them to evaluate a wide range of important hematic pathologies such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and time consuming. An automatic detection and classification of WBC images can enhance the accuracy and speed up the detection of WBCs. In this paper, we propose an efficient framework for localization of WBCs within microscopic blood smear images using a multi-class ensemble classification mechanism. In the proposed framework, the nuclei are first segmented, followed by extraction of features such as texture, statistical, and wavelet features. Finally, the detected WBCs are classified into five classes including basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on a natural (non-synthetic) benchmark database validate the effectiveness and efficiency of the proposed system in contrast to state-of-the-art schemes.