MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security (GameSec), 201

Traffic signs recognition for detailed digital maps development and driver assistance systems

Digital maps are considered as an additional sensor in many of the new ADAS, but these systems usually require a higher level of accuracy and detail of the maps. Among the important information that the maps should contain are the road geometry and traffic signs. In the first case, it is interesting to use accurate and fast methods for measurement. In the paper, a method based on a datalog vehicle is used. Satellite positioning and inertial measurements systems data are combined and dynamic behavior of the vehicle body is corrected measuring the movements of the suspension system. On the other hand, the information provided by traffic signs and route-guidance signs is extremely important for safe and successful driving. An automatic system that is capable of extracting and identifying these signs automatically would help human drivers enormously; navigation would be easier, allowing them to concentrate on driving the vehicle. A Computer Vision System is used to recognize and classify the different families of traffic signs combining it with GPS information to develop detailed and accurate digital maps. This sign recognition can also be used for real time warnings to the driver. Some results of test carried out in real situations are shown

Stereo visual odometry in urban environments based on detecting ground features

publisher: Elsevier articletitle: Stereo visual odometry in urban environments based on detecting ground features journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.03.004 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.publisher: Elsevier articletitle: Stereo visual odometry in urban environments based on detecting ground features journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.03.004 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Análisis de la adhesión de recubrimientos del sistema Y2O3-Al2O3-SiO2 sobre sustratos de interés para la industria aeroespacial

En la industria aeroespacial se necesitan materiales ligeros que tengan unas altas prestaciones mecánicas combinadas con una baja densidad. El carburo de silicio, el carbono reforzado con fibra de carbono y el carburo de silicio reforzado con fibra de carbono son materiales que cumplen con estos requisitos, pero a altas temperaturas presentan problemas de oxidación. Una de las formas más efectivas de prevenir este fenómeno es la utilización de recubrimientos cerámicos, cuya correcta adhesión sobre los distintos sustratos es fundamental para garantizar su funcionamiento. En el caso del presente trabajo, se analiza la adhesión de recubrimientos vítreos del sistema Y2O3-Al2O3-SiO2 obtenidos mediante proyección térmica por llama oxiacetilénica. Para ello, se realizan ensayos de rayado a carga creciente analizando el tipo y la carga de fallo y su relación con las propiedades elásticas y mecánicas de los recubrimientos. Los resultados indican que la adhesión sobre los sustratos carburo de silicio y carburo de silicio reforzado con fibra de carbono es buena, mientras que el carbono reforzado con fibra de carbono no es un material adecuado para recubrir

Identification of evolutionary trajectories shared across human betacoronaviruses

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, whilst the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), whilst a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1 and SARS-CoV-2), we developed a methodological pipeline to classify shared non-synonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection, and draw upon protein structure data to identify potential biological implications. We find 30 candidate mutations, from which four [codon sites 18121 (nsp14/residue 28), 21623 (spike/21), 21635 (spike/25) and 23948 (spike/796); SARS-CoV-2 genome numbering] further display evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance

Rationale and design of the pragmatic clinical trial tREatment with Beta-blockers after myOcardial infarction withOut reduced ejection fracTion (REBOOT).

There is a lack of evidence regarding the benefits of β-blocker treatment after invasively managed acute myocardial infarction (MI) without reduced left ventricular ejection fraction (LVEF). The tREatment with Beta-blockers after myOcardial infarction withOut reduced ejection fracTion (REBOOT) trial is a pragmatic, controlled, prospective, randomized, open-label blinded endpoint (PROBE design) clinical trial testing the benefits of β-blocker maintenance therapy in patients discharged after MI with or without ST-segment elevation. Patients eligible for participation are those managed invasively during index hospitalization (coronary angiography), with LVEF >40%, and no history of heart failure (HF). At discharge, patients will be randomized 1:1 to β-blocker therapy (agent and dose according to treating physician) or no β-blocker therapy. The primary endpoint is a composite of all-cause death, non-fatal reinfarction, or HF hospitalization over a median follow-up period of 2.75 years (minimum 2 years, maximum 3 years). Key secondary endpoints include the incidence of the individual components of the primary composite endpoint, the incidence of cardiac death, and incidence of malignant ventricular arrhythmias or resuscitated cardiac arrest. The primary endpoint will be analysed according to the intention-to-treat principle. The REBOOT trial will provide robust evidence to guide the prescription of β-blockers to patients discharged after MI without reduced LVEF.REBOOT is a non-commercial trial whose main sponsor is the Spanish National Center for Cardiovascular Research (CNIC). The study also received partial funding from the BI group through the CIBERCV network.S