MMJan 7, 2016
A New Image Steganographic Technique using Pattern based Bits Shuffling and Magic LSB for Grayscale ImagesKhan Muhammad, Jamil Ahmad, Haleem Farman et al.
Image Steganography is a growing research area of information security where secret information is embedded in innocent-looking public communication. This paper proposes a novel crystographic technique for grayscale images in spatial domain. The secret data is encrypted and shuffled using pattern based bits shuffling algorithm (PBSA) and a secret key. The encrypted data is then embedded in the cover image using magic least significant bit (M-LSB) method. Experimentally, the proposed method is evaluated by qualitative and quantitative analysis which validates the effectiveness of the proposed method in contrast to several state-of-the-art methods.
CROct 1, 2015
An Adaptive Secret Key-directed Cryptographic Scheme for Secure Transmission in Wireless Sensor NetworksKhan Muhammad, Zahoor Jan, Jamil Ahmad et al.
Wireless Sensor Networks (WSNs) are memory and bandwidth limited networks whose main goals are to maximize the network lifetime and minimize the energy consumption and transmission cost. To achieve these goals, dif ferent techniques of compression and clustering have been used. However, security is an open and major issue in WSNs for which different approaches are used, both in centralized and distributed WSNs' environments. This paper presents an adaptive cryptographic scheme for secure transmission of various sensitive parameters, sensed by wireless sensors to the fusion center for further processing in WSNs such as military networks. The proposed method encrypts the sensitive captured data of sensor nodes using various encryption procedures (bitxor operation, bits shuffling, and secret key based encryption) and then sends it to the fusion center. At the fusion center, the received encrypted data is decrypted for taking further necessary actions. The experimental results with complexity analysis, validate the effectiveness and feasibility of the proposed method in terms of security in WSNs.
MMFeb 27, 2015
A Secure Cyclic Steganographic Technique for Color Images using RandomizationKhan Muhammad, Jamil Ahmad, Naeem Ur Rehman et al.
Information Security is a major concern in today's modern era. Almost all the communicating bodies want the security, confidentiality and integrity of their personal data. But this security goal cannot be achieved easily when we are using an open network like Internet. Steganography provides one of the best solutions to this problem. This paper represents a new Cyclic Steganographic T echnique (CST) based on Least Significant Bit (LSB) for true color (RGB) images. The proposed method hides the secret data in the LSBs of cover image pixels in a randomized cyclic manner. The proposed technique is evaluated using both subjective and objective analysis using histograms changeability, Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). Experimentally it is found that the proposed method gives promising results in terms of security, imperceptibility and robustness as compared to some existent methods and vindicates this new algorithm.
CVJun 16, 2014
A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes RecognitionJamil Ahmad, Zahoor Jan, Zia-ud-Din et al.
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.