The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain.

Messages posted to online social networks (OSN) causes a recent stir due to the intended spread of fake news or rumor. This work aims to understand and analyse the characteristics of fake news especially in relation to sentiments, for the automatic detection of fake news and rumors. Based on empirical observations, we propose a hypothesis that there exists a relation between fake messages or rumours and sentiments of the texts posted online. We verify our hypothesis by comparing with the state-of-the-art baseline text-only fake news detection methods that do not consider sentiments. We performed experiments on standard Twitter fake news dataset and show good improvements in detecting fake news or rumor posts.

Ambient assisted living (AAL) has the ambitious goal of improving the quality of life and maintaining independence of older and vulnerable people through the use of technology. Most of the western world will see a very large increase in the number of older people within the next 50 years with limited resources to care for them. AAL is seen as a promising alternative to the current care models and consequently has attracted lots of attention. Recently, a number of researchers have developed solutions based on video cameras and computer vision systems with promising results. However, for the domain to reach maturity, several challenges need to be faced, including the development of systems that are robust in the real-world and are accepted by users, carers and society. In this literature review paper we present a comprehensive survey of the scope of the domain, the existing technical solutions and the challenges to be faced.

Abstract-A distributed and tamper proof media transaction framework is proposed based on the blockchain model. Current multimedia distribution does not preserve self-retrievable information of transaction trails or content modification histories. For example, digital copies of valuable artworks, creative media and entertainment contents are distributed for various purposes including exhibitions, gallery collections or in media production workflow. Original media is often edited for creative content preparation or tampered with to fabricate false propaganda over social media. However there is no existing trusted mechanism that can easily retrieve either the transaction trails or the modification histories. We propose a novel watermarking based Multimedia Blockchain framework that can address such issues. The unique watermark information contains two pieces of information: a) a cryptographic hash that contains transaction histories (blockchain transactions log) and b) an image hash that preserves retrievable original media content. Once the watermark is extracted, first part of the watermark is passed to a distributed ledger to retrieve the historical transaction trail and the latter part is used to identify the edited / tampered regions. The paper outlines the requirements, the challenges and demonstrates the proof of this concept.

Imperceptibility and robustness are two complementary but fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host media. If distortion due to high strength watermarking can avoid visually attentive regions, such distortions are unlikely to be noticeable to any viewer. In this paper, we exploit this concept and propose a novel visual attention-based highly robust image watermarking methodology by embedding lower and higher strength watermarks in visually salient and non-salient regions, respectively. A new low complexity wavelet domain visual attention model is proposed that allows us to design new robust watermarking algorithms. The proposed new saliency model outperforms the state-of-the-art method in joint saliency detection and low computational complexity performances. In evaluating watermarking performances, the proposed blind and non-blind algorithms exhibit increased robustness to various natural image processing and filtering attacks with minimal or no effect on image quality, as verified by both subjective and objective visual quality evaluation. Up to 25% and 40% improvement against JPEG2000 compression and common filtering attacks, respectively, are reported against the existing algorithms that do not use a visual attention model.