The widespread use of social media has enormous consequences for the society, culture and business with potentially positive and negative effects. As online social networks are increasingly used for dissemination of information, at the same they are also becoming a medium for the spread of fake news for various commercial and political purposes. Technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP) tools offer great promise for researchers to build systems, which could automatically detect fake news. However, detecting fake news is a challenging task to accomplish as it requires models to summarize the news and compare it to the actual news in order to classify it as fake. This project proposes a framework that detects and classifies fake news messages using improved Recurrent Neural Networks and Deep Structured Semantic Model. The proposed approach intuitively identifies important features associated with fake news without previous domain knowledge while achieving accuracy 99%. The performance analysis method used for the proposed system is based on accuracy, specificity and sensitivity.

Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.

Here we are presenting some novel techniques for squirting colors in grayscale images. The problem of coloring grayscale images has no exact solution. Here we are attempting to minimize the human efforts needed in manually coloring the grayscale images. We need human interaction only to find a reference color image, then the job of transferring color traits from reference color image to grayscale image is done by proposed techniques. In these techniques, the color palette is prepared using pixel windows of some degrees taken from reference color image. Then the grayscale image is divided into pixel windows with same degrees. For every window of grayscale image the palette is searched for equivalent color values, which could be used to color grayscale window. In the whole process the luminance values of reference color image and target grayscale image are only matched and based on best possible match the respective chromaticity values of color image are transferred to grayscale image. For palette preparation first we used RGB color space and then Kekre's LUV color space[9]. Results with Kekre's LUV color space were comparatively better. To improve the searching time through color palette the exhaustive and Kekre's fast search are used.

Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.