Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems

Mis- and disinformation are a substantial global threat to our security and safety. To cope with the scale of online misinformation, researchers have been working on automating fact-checking by retrieving and verifying against relevant evidence. However, despite many advances, a comprehensive evaluation of the possible attack vectors against such systems is still lacking. Particularly, the automated fact-verification process might be vulnerable to the exact disinformation campaigns it is trying to combat. In this work, we assume an adversary that automatically tampers with the online evidence in order to disrupt the fact-checking model via camouflaging the relevant evidence or planting a misleading one. We first propose an exploratory taxonomy that spans these two targets and the different threat model dimensions. Guided by this, we design and propose several potential attack methods. We show that it is possible to subtly modify claim-salient snippets in the evidence and generate diverse and claim-aligned evidence. Thus, we highly degrade the fact-checking performance under many different permutations of the taxonomy's dimensions. The attacks are also robust against post-hoc modifications of the claim. Our analysis further hints at potential limitations in models' inference when faced with contradicting evidence. We emphasize that these attacks can have harmful implications on the inspectable and human-in-the-loop usage scenarios of such models, and conclude by discussing challenges and directions for future defenses

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

VISION AND NATURAL LANGUAGE FOR CREATIVE APPLICATIONS, AND THEIR ANALYSIS

Recent advances in machine learning, specifically problems in Computer Vision and Natural Language, have involved training deep neural networks with enormous amounts of data. The first frontier for deep networks was in uni-modal classification and detection problems (which were directed more towards ”intelligent robotics” and surveillance applications), while the next wave involves deploying deep networks on more creative tasks and common-sense reasoning. We provide two applications of these, interspersed by an analysis on these deep models. Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in different captions. We present two different architectures for language-conditioned colorization, both of which produce more accurate and plausible colorizations than a language-agnostic version. Through this language-based framework, we can dramatically alter colorizations by manipulating descriptive color words in captions. Researchers have observed that Visual Question Answering(VQA) models tend to answer questions by learning statistical biases in the data. (for example, the answer to the question “What is the color of the sky?” is usually “Blue”). It is of interest to the community to explicitly discover such biases, both for understanding the behavior of such models, and towards debugging them. In a database, we store the words of the question, answer and visual words corresponding to regions of interest in attention maps. By running simple rule mining algorithms on this database, we discover human-interpretable rules which give us great insight into the behavior of such models. Our results also show examples of unusual behaviors learned by the model in attempting VQA tasks. Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the gutters between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called closure. While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We construct a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language. For many NLP tasks, ordered models, which explicitly encode word order information, do not significantly outperform unordered (bag-of-words) models. One potential explanation is that the tasks themselves do not require word order to solve. To test whether this explanation is valid, we perform several time-controlled human experiments with scrambled language inputs. We compare human accuracies to those of both ordered and unordered neural models. Our results contradict the initial hypothesis, suggesting instead that humans may be less robust to word order variation than computers

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

Scene understanding for interactive applications

Para interactuar con el entorno, es necesario entender que está ocurriendo en la escena donde se desarrolla la acción. Décadas de investigación en el campo de la visión por computador han contribuido a conseguir sistemas que permiten interpretar de manera automática el contenido en una escena a partir de información visual. Se podría decir el objetivo principal de estos sistemas es replicar la capacidad humana para extraer toda la información a partir solo de datos visuales. Por ejemplo, uno de sus objetivos es entender como percibimosel mundo en tres dimensiones o como podemos reconocer sitios y objetos a pesar de la gran variación en su apariencia. Una de las tareas básicas para entender una escena es asignar un significado semántico a cada elemento (píxel) de una imagen. Esta tarea se puede formular como un problema de etiquetado denso el cual especifica valores (etiquetas) a cada pixel o región de una imagen. Dependiendo de la aplicación, estas etiquetas puedenrepresentar conceptos muy diferentes, desde magnitudes físicas como la información de profundidad, hasta información semántica, como la categoría de un objeto. El objetivo general en esta tesis es investigar y desarrollar nuevas técnicas para incorporar automáticamente una retroalimentación por parte del usuario, o un conocimiento previo en sistemas inteligente para conseguir analizar automáticamente el contenido de una escena. en particular,esta tesis explora dos fuentes comunes de información previa proporcionado por los usuario: interacción humana y etiquetado manual de datos de ejemplo.La primera parte de esta tesis esta dedicada a aprendizaje de información de una escena a partir de información proporcionada de manera interactiva por un usuario. Las soluciones que involucran a un usuario imponen limitaciones en el rendimiento, ya que la respuesta que se le da al usuario debe obtenerse en un tiempo interactivo. Esta tesis presenta un paradigma eficiente que aproxima cualquier magnitud por píxel a partir de unos pocos trazos del usuario. Este sistema propaga los escasos datos de entrada proporcionados por el usuario a cada píxel de la imagen. El paradigma propuesto se ha validado a través detres aplicaciones interactivas para editar imágenes, las cuales requieren un conocimiento por píxel de una cierta magnitud, con el objetivo de simular distintos efectos.Otra estrategia común para aprender a partir de información de usuarios es diseñar sistemas supervisados de aprendizaje automático. En los últimos años, las redes neuronales convolucionales han superado el estado del arte de gran variedad de problemas de reconocimiento visual. Sin embargo, para nuevas tareas, los datos necesarios de entrenamiento pueden no estar disponibles y recopilar suficientes no es siempre posible. La segunda parte de esta tesis explora como mejorar los sistema que aprenden etiquetado denso semántico a partir de imágenes previamente etiquetadas por los usuarios. En particular, se presenta y validan estrategias, basadas en los dos principales enfoques para transferir modelos basados en deep learning, para segmentación semántica, con el objetivo de poder aprender nuevas clases cuando los datos de entrenamiento no son suficientes en cantidad o precisión.Estas estrategias se han validado en varios entornos realistas muy diferentes, incluyendo entornos urbanos, imágenes aereas y imágenes submarinas.In order to interact with the environment, it is necessary to understand what is happening on it, on the scene where the action is ocurring. Decades of research in the computer vision field have contributed towards automatically achieving this scene understanding from visual information. Scene understanding is a very broad area of research within the computer vision field. We could say that it tries to replicate the human capability of extracting plenty of information from visual data. For example, we would like to understand how the people perceive the world in three dimensions or can quickly recognize places or objects despite substantial appearance variation. One of the basic tasks in scene understanding from visual data is to assign a semantic meaning to every element of the image, i.e., assign a concept or object label to every pixel in the image. This problem can be formulated as a dense image labeling problem which assigns specific values (labels) to each pixel or region in the image. Depending on the application, the labels can represent very different concepts, from a physical magnitude, such as depth information, to high level semantic information, such as an object category. The general goal in this thesis is to investigate and develop new ways to automatically incorporate human feedback or prior knowledge in intelligent systems that require scene understanding capabilities. In particular, this thesis explores two common sources of prior information from users: human interactions and human labeling of sample data. The first part of this thesis is focused on learning complex scene information from interactive human knowledge. Interactive user solutions impose limitations on the performance where the feedback to the user must be at interactive rates. This thesis presents an efficient interaction paradigm that approximates any per-pixel magnitude from a few user strokes. It propagates the sparse user input to each pixel of the image. We demonstrate the suitability of the proposed paradigm through three interactive image editing applications which require per-pixel knowledge of certain magnitude: simulate the effect of depth of field, dehazing and HDR tone mapping. Other common strategy to learn from user prior knowledge is to design supervised machine-learning approaches. In the last years, Convolutional Neural Networks (CNNs) have pushed the state-of-the-art on a broad variety of visual recognition problems. However, for new tasks, enough training data is not always available and therefore, training from scratch is not always feasible. The second part of this thesis investigates how to improve systems that learn dense semantic labeling of images from user labeled examples. In particular, we present and validate strategies, based on common transfer learning approaches, for semantic segmentation. The goal of these strategies is to learn new specific classes when there is not enough labeled data to train from scratch. We evaluate these strategies across different environments, such as autonomous driving scenes, aerial images or underwater ones.<br /

C13orf31 (FAMIN) is a central regulator of immunometabolic function.

Single-nucleotide variations in C13orf31 (LACC1) that encode p.C284R and p.I254V in a protein of unknown function (called 'FAMIN' here) are associated with increased risk for systemic juvenile idiopathic arthritis, leprosy and Crohn's disease. Here we set out to identify the biological mechanism affected by these coding variations. FAMIN formed a complex with fatty acid synthase (FASN) on peroxisomes and promoted flux through de novo lipogenesis to concomitantly drive high levels of fatty-acid oxidation (FAO) and glycolysis and, consequently, ATP regeneration. FAMIN-dependent FAO controlled inflammasome activation, mitochondrial and NADPH-oxidase-dependent production of reactive oxygen species (ROS), and the bactericidal activity of macrophages. As p.I254V and p.C284R resulted in diminished function and loss of function, respectively, FAMIN determined resilience to endotoxin shock. Thus, we have identified a central regulator of the metabolic function and bioenergetic state of macrophages that is under evolutionary selection and determines the risk of inflammatory and infectious disease.Supported by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement 260961, the Wellcome Trust (investigator award 106260/Z/14/Z; a PhD fellowship for clinicians; and a Career Re-Entry Fellowship), the Wellcome Trust Sanger Institute, the US National Institutes of Health (5U420D011174 and 5U54HG006348), the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research Cambridge Biomedical Research Centre, the European Crohn’s and Colitis Organisation and the Swedish Medical Research Council and the Olle Engkvist foundation.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ni.353