Avances en Informática y Automática. Decimotercer workshop

Actas de los trabajos de TFM del Máster Universitario en Sistemas Inteligentes 2018-2019[ES]El Máster Oficial en Sistemas Inteligentes de la Universidad de Salamanca tiene como principal objetivo promover la iniciación de los estudiantes en el ámbito de la investigación. El congreso organizado por el Departamento de Informática y Automática que se celebra dentro del Máster en Sistemas Inteligentes de la Universidad de Salamanca proporciona la oportunidad ideal para que sus estudiantes presenten los principales resultados de sus Trabajos de Fin de Máster y obtengan una realimentación del interés de los mismos. La decimotercera edición del workshop «Avances en Informática y Automática», correspondiente al curso 2018-2019, ha sido un encuentro interdisciplinar donde se han presentado trabajos perte-necientes a un amplio abanico de líneas de investigación. Todos los trabajos han sido supervisados por investigadores de reconocido prestigio pertenecientes a la Universidad de Salamanca, propor-cionando el marco idóneo para sentar las bases de una futura tesis doctoral. Entre los principales objetivos del congreso se encuentran: -Ofrecer a los estudiantes un marco donde exponer sus primeros trabajos de investigación. -Proporcionar a los participantes un foro donde discutir ideas y encontrar nuevas sugerencias de compañeros, investigadores y otros asistentes a la reunión. -Permitir a cada estudiante una realimentación de los participantes sobre su trabajo y una orientación sobre las futuras direcciones de investigación. -Contribuir al desarrollo del espíritu de colaboración en la investigación

Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach works successfully in a number of practical scenarios, including (but not limited to) observations of light effusions through the windows, on the back wall, or off the victim’s face. My empirical results show that I can successfully confirm hypotheses while capturing short recordings (typically less than 4 minutes long) of the changes in brightness from the victim’s display from a distance of 70 meters. Lastly, for content-based copy detection, I take advantage of a new temporal feature to index a reference library in a manner that is robust to the popular spatial and temporal transformations in pirated videos. My technique narrows the detection gap in the important area of temporal transformations applied by would-be pirates. My large-scale evaluation on real-world data shows that I can successfully detect infringing content from movies and sports clips with 90.0% precision at a 71.1% recall rate, and can achieve that accuracy at an average time expense of merely 5.3 seconds, outperforming the state of the art by an order of magnitude.Doctor of Philosoph