Nishchol Mishra

A secure video steganography with encryption based on LSB technique

Need of hiding information from intruders has been around since ancient times. Nowadays Digital media is getting advanced like text, image, audio, video etc. To maintain the secrecy of information, different methods of hiding have been evolved. One of them is Steganography, which means hiding information under some other information without noticeable change in cover information. Recently Video Steganography has become a boon for providing large amount of data to be transferred secretly. Video is simply a sequence of images, hence much space is available in between for hiding information. In proposed scheme video steganography is used to hide a secret video stream in cover video stream. Each frame of secret video will be broken into individual components then converted into 8-bit binary values, and encrypted using XOR with secret key and encrypted frames will be hidden in the least significant bit of each frames using sequential encoding of Cover video. To enhance more security each bit of secret frames will be stored in cover frames following a pattern BGRRGBGR.

Analysis of Different Similarity Measure Functions and Their Impacts on Shared Nearest Neighbor Clustering Approach

Clustering is a technique of grouping data with analogous data content. In recent years, Density based clustering algorithms especially SNN clustering approach has gained high popularity in the field of data mining. It finds clusters of different size, density, and shape, in the presence of large amount of noise and outliers. SNN is widely used where large multidimensional and dynamic databases are maintained. A typical clustering technique utilizes similarity function for comparing various data items. Previously, many similarity functions such as Euclidean or Jaccard similarity measures have been worked upon for the comparison purpose. In this paper, we have evaluated the impact of four different similarity measure functions upon Shared Nearest Neighbor (SNN) clustering approach and the results were compared subsequently. Based on our analysis, we arrived on a conclusion that Euclidean function works best with SNN clustering approach in contrast to cosine, Jaccard and correlation distance measures function.

Survey of Influential User Identification Techniques in Online Social Networks

Online social networks became a remarkable development with wonderful social as well as economic impact within the last decade. Currently the most famous online social network, Facebook, counts more than one billion monthly active users across the globe. Therefore, online social networks attract a great deal of attention among practitioners as well as research communities. Taken together with the huge value of information that online social networks hold, numerous online social networks have been consequently valued at billions of dollars. Hence, a combination of this technical and social phenomenon has evolved worldwide with increasing socioeconomic impact. Online social networks can play important role in viral marketing techniques, due to their power in increasing the functioning of web search, recommendations in various filtering systems, scattering a technology (product) very quickly in the market. In online social networks, among all nodes, it is interesting and important to identify a node which can affect the behaviour of their neighbours; we call such node as Influential node. The main objective of this paper is to provide an overview of various techniques for Influential User identification. The paper also includes some techniques that are based on structural properties of online social networks and those techniques based on content published by the users of social network.

Towards the growth of image encryption and authentication schemes

This paper is simply the gathering of recent developments in the field of image security and presents further improvements in the same field. Images are the most important utility of our life. They are used in many applications. There are two main goals of image security: image encryption and authentication. During the past years, several image encryption and authentication algorithms have been proposed. Image encryption techniques scramble the pixels of the image and decrease the correlation among the pixels, such that the encrypted image cannot be accessed by unauthorized user. Chaotic encryption method seems to be much better day by day. Chaotic encryption technique is the new way of cryptography. Many chaos-based encryption methods have been proposed in the last decade. This paper presents a survey of different chaotic image encryption schemes proposed in the last decade. The paper also presents different image encryption and authentication schemes and discusses the problems and resolution associated with them. Emphasizing the image security, the paper discusses a hybrid scheme for image encryption and authentication, such that further advances in the field of image security can be enhanced.

Region Duplication Forgery Detection Technique Based on SURF and HAC

Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

Survey and analysis of temporal link prediction in online social networks

Online Social Network has tremendously captured the attention of users in recent years. Some social networking sites are explicitly designed for social interaction, while some of them are application based providing content sharing along with social communication. Link prediction is a new interdisciplinary research direction in which, existing links are analyzed and future links are predicted among millions of users of social network. Traditional link prediction methods had focused on the use of graph metrics to determine, where new links are likely to arise. A small amount of work has been done on analyzing longterm graph trends. This paper does the survey and analysis of temporal evolution of link prediction. It has been found out that the earlier graph generation models were unrealistic in their prediction and can be complemented through the use of temporal metrics, resulting in a highly accurate link prediction. In addition, the paper is concluded with the proposed framework which exploits temporal feature along with local similarity attributes.

Prevention of traffic analysis attack in friend in the middle using dummy traffic approach

Online social network is a platform which makes each individual to be connected with their friends and colleagues. This connectivity encourages the users to share their private information all the way through social networking sites but sharing of personal information on this open platform leads to concern of privacy issues. Personal information available in social networking sites makes the adversary to take undue advantage of data and harm the user by embarrassing them or ruining their reputation. Traffic Analysis is one of the privacy issues which mean pilfering the personal communication between two users. Friend in the Middle (FiM) approach focuses on providing privacy awake social network architecture and extends the current OSNs in such a way that it restricts unnecessary access to the information but it is prone to traffic analysis attacks. In the proposed scheme, dummy traffic approach is used to prevent traffic analysis attack in Friend in the Middle (FiM). Comparison between the existing FiM and the proposed FiM is made using various parameters and enhanced accuracy of the proposed approach with the existing technique is achieved.

Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm

There are various new applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. However, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. There is always need to efficiently store and retrieve image data to perform assigned tasks and to make a decision. This paper presents a new image retrieval framework with two types of relevance feedback i.e., implicit feedback in combination with explicit feedback. This paper employs Interactive Genetic Approach to discover a combination of descriptors that better characterizes the user perception of image resemblance. This approach provides better management and retrieval of images than the keyword-based approach. However, most of the conventional methods do not have the capability to effectively incorporate human interaction and emotion into retrieving images. In order to solve this problem we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). In this approach we used two tier architecture of implicit and explicit feedback with IGA. Therefore, this system facilitates the search for the image not only with explicit queries, but also with implicit queries.

Survey of various techniques for determining influential users in social networks

Into the bloggers or a blog network, there are some users who cause a great influence over other users of the network. In this paper we refer these kinds of users as Influential Users (IU). IUs are those users that cause the other users to do some actions on the documents and contents published by him or her. The IU is being used by different organizations for viral marketing by using blogging sites. The organization wants to market a new product by using a small group of potential users to get profit. In this paper, we focus on the various approaches that helps in determination of IUs, some of them are based on the topology of the social network and some are based on hyperlink and later we discuss the new approach to finding the influential user which is based on the activities that the users performs in social networks, utilizing their diffusion history.

Multi-Relation Image Retrieval and Annotation based on Holistic Approach & Decision Tree

work of multi-relation image retrieval and annotation is based on the holistic approach and the decision tree. In this we have proposed that for the retrieval of similar images as that of query image Dominant color descriptor (DCD) is used, this descriptor uses the color feature for the retrieval of images. This creates the feature vector index. Test keywords are correlated with the feature vector index, the correlation is performed by multi class association to get the classes for processing on them. Classes which are not necessary are discarded using cross validation in the decision tree process Decision tree used to take the relevant classes and finally we calculate the Gain of feature vector and we get the retrieval of the images based on the query image with the associated keywords or annotation. This work has been implemented on MatLab 7.5 simulator.