Automatic stereoscopic video object-based watermarking using qualified significant wavelet trees

In this paper a fully automatic scheme for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. During the first module unsupervised video object extraction is performed, by analyzing stereoscopic pairs of frames. In the second module each video object is decomposed into three levels with ten subbands, using the Shape Adaptive Discrete Wavelet Transform (SA-DWT) and three pairs of subbands are formed (HL3 , HL2), (LH3, LH2) and (HH3, HH2). Next Qualified Significant Wavelet Trees (QSWTs) are estimated for the specific pair of subbands with the highest energy content. QSWTs are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy paths of wavelet coefficients. Finally during the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse SA-DWT is applied to provide the watermarked video object. Performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of noise. Furthermore the case of transmission losses for the watermarked video objects is also investigated. Experimental results on real life video objects indicate the efficiency and robustness of the proposed schemeFacultad de Informátic

Remote authentication via biometrics: a robust video-object steganographic mechanism over wireless networks

In wireless communications sensitive information is frequently exchanged, requiring remote authentication. Remote authentication involves the submission of encrypted information, along with visual and audio cues (facial images/videos, human voice etc.). Nevertheless, Trojan Horse and other attacks can cause serious problems, especially in cases of remote examinations (in remote studying) or interviewing (for personnel hiring). This paper proposes a robust authentication mechanism based on semantic segmentation, chaotic encryption and data hiding. Assuming that user X wants to be remotely authenticated, initially X’s video object (VO) is automatically segmented, using a headand-body detector. Next, one of X’s biometric signals is encrypted by a chaotic cipher. Afterwards the encrypted signal is inserted to the most significant wavelet coefficients of the VO, using its Qualified Significant Wavelet Trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical in wireless networks. Finally, the Inverse Discrete Wavelet Transform (IDWT) is applied to provide the stego-object (SO). Experimental results, regarding: (a) security merits of the proposed encryption scheme, (b) robustness to steganalytic attacks, to various transmission losses and JPEG compression ratios and (c) bandwidth efficiency measures, indicate the promising performance of the proposed biometrics-based authentication scheme

Multiresolution organization of social media users' profiles: fast detection and efficient transmission of characteristic profiles

In this paper, multiresolution organization of social media users' profiles is proposed that enables fast browsing and effective transmission of characteristic profiles. The scheme results in the construction of a novel three-layer tree structure. At layer 0 the root-node contains the most representative profile among class representatives (CRs). Each node of layer 1 represents a class of profiles, while in layer 2 the full resolution is available (all profiles). The nodes of the proposed tree structure contain viewing elements and in this paper we focus on the extraction of viewing elements for layers 0, and 1. Towards this direction the viewing elements of layer 1 are optimally extracted using an interpolation method, while the viewing element of layer 0 is estimated from the viewing elements of layer 1. The resulting tree-structure enables users to quickly browse and detect profiles of interest, by selecting the viewing elements they like. Experimental results on reallife social media users indicate the promising performance of this innovative scheme

Wall-content selection in social media: a revelance feedback scheme based on explicit crowdsourcing

This paper proposes an innovative relevance feedback algorithm for wall-content selection in social media. The procedure results in an iterative loop, which recursively updates a weighted distance. The distance is then used for finding multimedia items that are relevant to a user's preferences. To do so, the activity log of the user under investigation is considered and his/her attention at previous intervals is analyzed. Another novel point of the proposed approach is the incorporation of friends' attention into the relevance feedback scheme. In particular, interactions among users and posted multimedia items are considered as an explicit crowdsourcing activity. By this way some multimedia items receive more attention, while some others receive less or no attention. By analyzing these social interactions, a social computing framework is formed, which affects the evolution of the content selection process. Overall, the iterative relevance feedback algorithm takes into consideration visual features, activity log and social attention, in order to select the wall information of each social media user. Experimental results and comparisons on real data, exhibit the advantages of the proposed scheme and future directions are also discussed

Unsupervised sports video particles annotation based on social latent semantic analysis

Large volumes of video particles on practically every major sports event are posted on social media. According to socialbakers.com [1], four of the top twenty Facebook pages focus on sports. These particles can be processed and automatically annotated with events, entities etc. Furthermore, several annotated particles referring to a different time interval of the same sports event, could be synchronized to accomplish annotation of full sports games. Towards this direction, in this paper an innovative scheme is proposed that performs unsupervised annotation of sports video particles, posted on social media. The scheme is based on an intelligent wrapper architecture that automatically gathers and segments content and on the newly introduced Social Latent Semantic Analysis. This paper forms an initial study of automatic sports video particles annotation and experiments indicate its promising performance

IoT-Enabled Ambulances Assisting Citizens' Well-Being after Earthquake Disasters in Smart Cities

Physical disasters can highly impact the well-being of people living in Smart Cities (SCs). Many citizens, including children and seniors, can face serious issues and survival risks after a physical disaster. Internet of Things (IoT) technology can be used to mitigate such risks by providing the technological solution to assist citizens in extreme situations. Such extreme physical disasters are earthquakes. In earthquakes houses and public buildings may collapse and capture many individuals in the ruins. Lives of citizens are seriously threatened. After an earthquake, in a hostile environment, ambulances should take control and provide solutions to fundamental human needs. In this paper an Emergency Medical Service (EMS) system is proposed, enabled with IoT technology. Our system incorporates, sensors and IoT equipment embedded to ambulances to assist and give solutions to peoples' problems after an earthquake. Specifically two use cases are examined and a dynamic routing algorithm is proposed to face the problem. The overall scheme is evaluated using synthetic data from the SC of Manchester, United Kingdom. Certain metrics are introduced to compare the proposed use cases in the context of the emerging SCs technological environments