Jamil Ahmad

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.

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.

Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems

Content based image retrieval systems rely heavily on the set of features extracted from images. Effective image representation emerges as a crucial step in such systems. A key challenge in visual content representation is to reduce the so called ‘semantic gap’. It is the inability of existing methods to describe contents in a human-oriented way. Content representation methods inspired by the human vision system have shown promising results in image retrieval. Considerable work has been carried out during the past two decades for developing methods to extract descriptors inspired by the human vision system and attempt to retrieve visual contents efficiently according to the user needs, thereby reducing the semantic gap. Despite the extensive research being conducted in this area, limitations in current image retrieval systems still exist. This paper presents a descriptor for personalized social image collections which utilizes the local structure patterns in salient edge maps of images at multiple scales. The human visual system at the basic level is sensitive to edges, corners, intersections, and other such intensity variations in images generating local structure patterns. Analyzing these patterns at multiple scales allow the most salient fine-grained and coarse-grained features to be captured. The features are accumulated in a local structure patterns histogram to index images allowing flexible querying of visual contents. The retrieval results show that the proposed descriptor ranks well among similar state-of-the-art methods for large social image collections.

Visual saliency models for summarization of diagnostic hysteroscopy videos in healthcare systems

In clinical practice, diagnostic hysteroscopy (DH) videos are recorded in full which are stored in long-term video libraries for later inspection of previous diagnosis, research and training, and as an evidence for patients’ complaints. However, a limited number of frames are required for actual diagnosis, which can be extracted using video summarization (VS). Unfortunately, the general-purpose VS methods are not much effective for DH videos due to their significant level of similarity in terms of color and texture, unedited contents, and lack of shot boundaries. Therefore, in this paper, we investigate visual saliency models for effective abstraction of DH videos by extracting the diagnostically important frames. The objective of this study is to analyze the performance of various visual saliency models with consideration of domain knowledge and nominate the best saliency model for DH video summarization in healthcare systems. Our experimental results indicate that a hybrid saliency model, comprising of motion, contrast, texture, and curvature saliency, is the more suitable saliency model for summarization of DH videos in terms of extracted keyframes and accuracy.

A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model

2 Abstract: Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods.

Image steganography for authenticity of visual contents in social networks

Social networks are major sources of image sharing and secret messaging among the people. To date, such networks are not strictly bounded by copyright laws due to which image sharing, secret messaging, and its authentication is vulnerable to many risks. In addition to this, maintaining the confidentiality, integrity, and authenticity of secret messages is an open challenge of today’s communication systems. Steganography is one of the solutions to tackle these problems. This paper proposes a secure crystographic framework for authenticity of visual contents using image steganography, utilizing color model transformation, three-level encryption algorithm (TLEA), and Morton scanning (MS)-directed least significant bit (LSB) substitution. The method uses I-plane of the input image in HSI for secret data embedding using MS-directed LSB substitution method. Furthermore, the secret data is encrypted using TLEA prior to embedding, adding an additional level of security for secure authentication. The qualitative and quantitative results verify the better performance of the proposed scheme and provide one of the best mechanisms for authenticity of visual contents in social networks.

Efficient object-based surveillance image search using spatial pooling of convolutional features

Display Omitted Presents an effective statistical method to determine optimal subset of convolution kernels.Spatial maximal activator pooling strategy to aggregate feature maps into a single feature map.Discriminative and low-dimensional representation allow efficient and accurate retrieval.Modularity of proposed framework allows its convenient enhancement via complicated pooling. Modern surveillance networks are large collections of computational sensor nodes, where each node can be programmed to capture, prioritize, segment salient objects, and transmit them to central repositories for indexing. Visual data from such networks grow exponentially and present many challenges concerning their transmission, storage, and retrieval. Searching for particular surveillance objects is a common but challenging task. In this paper, we present an efficient features extraction framework which utilizes an optimal subset of kernels from the first layer of a convolutional neural network pre-trained on ImageNet dataset for object-based surveillance image search. The input image is convolved with the set of kernels to generate feature maps, which are aggregated into a single feature map using a novel spatial maximal activator pooling approach. A low-dimensional feature vector is computed to represent surveillance objects. The proposed system provides improvements in both performance and efficiency over other similar approaches for surveillance datasets.

Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption

This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.

SSH: Salient Structures Histogram for Content Based Image Retrieval

Retrieving information accurately from large image collections is a challenging task due to the presence of complex patterns in visual data. High demands for efficient indexing and retrieval mechanisms is a major motivating factor for the extensive research in this area. Visual features of images are extracted and used for indexing, browsing and retrieval. The human visual system has a sensitivity to colors and edge orientations and hence, these features are very effective for representing a piece of visual information. In this paper, salient image structures corresponding to color and edge orientations are represented as a sparse feature matrix. It combines these features with saliency measures into a unified descriptor. The descriptor is tested on a large dataset. Experimental results verify the capability of the descriptor to represent color images with sufficient discrimination that will enable retrieval systems to perform better as compared to other state-of-the-art methods.

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.