On the use of SIFT features for face authentication

Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results

Understanding critical factors in gender recognition

Gender classification is a task of paramount importance in face recognition research, and it is potentially useful in a large set of applications. In this paper we investigate the gender classification problem by an extended empirical analysis on the Face Recognition Grand Challenge version 2.0 dataset (FRGC2.0). We propose challenging experimental protocols over the dimensions of FRGC2.0 – i.e., subject, face expression, race, controlled or uncontrolled environment. We evaluate our protocols with respect to several classification algorithms, and processing different types of features, like Gabor and LBP. Our results show that gender classification is independent from factors like the race of the subject, face expressions, and variations of controlled illumination conditions. We also report that Gabor features seem to be more robust than LBPs in the case of uncontrolled environment

WAF-A-MoLE: An adversarial tool for assessing ML-based WAFs

Abstract Web Application Firewalls (WAFs) are plug-and-play security gateways that promise to enhance the security of a (potentially vulnerable) system with minimal cost and configuration. In recent years, machine learning-based WAFs are catching up with traditional, signature-based ones. They are competitive because they do not require predefined rules; instead, they infer their rules through a learning process. In this paper, we present WAF-A-MoLE, a WAF breaching tool. It uses guided mutational-based fuzzing to generate adversarial examples. The main applications include WAF ( i ) penetration testing, ( i i ) benchmarking and ( i i i ) hardening

On the correlation between human fixations, handcrafted and CNN features

AbstractTraditional local image descriptors such as SIFT and SURF are based on processings similar to those that take place in the early visual cortex. Nowadays, convolutional neural networks still draw inspiration from the human vision system, integrating computational elements typical of higher visual cortical areas. Deep CNN's architectures are intrinsically hard to interpret, so much effort has been made to dissect them in order to understand which type of features they learn. However, considering the resemblance to the human vision system, no enough attention has been devoted to understand if the image features learned by deep CNNs and used for classification correlate with features that humans select when viewing images, the so-called human fixations, nor if they correlate with earlier developed handcrafted features such as SIFT and SURF. Exploring these correlations is highly meaningful since what we require from CNNs, and features in general, is to recognize and correctly classify objects or subjects relevant to humans. In this paper, we establish the correlation between three families of image interest points: human fixations, handcrafted and CNN features. We extract features from the feature maps of selected layers of several deep CNN's architectures, from the shallowest to the deepest. All features and fixations are then compared with two types of measures, global and local, which unveil the degree of similarity of the areas of interest of the three families. From the experiments carried out on ETD human fixations database, it turns out that human fixations are positively correlated with handcrafted features and even more with deep layers of CNNs and that handcrafted features highly correlate between themselves as some CNNs do

ZenHackAdemy: Ethical Hacking @ DIBRIS

Cybersecurity attacks are on the rise, and the current response is not effective enough. The need for a competent workforce, able to face attackers, is increasing. At the moment, the gap between academia and real-world skills is huge and academia cannot provide students with skills that match those of an attacker. To pass on these skills, teachers have to train students in scenarios as close as possible to real-world ones. Capture the Flag (CTF) competitions are a great tool to achieve this goal, since they encourage students to think as an attacker does, thus creating more awareness on the modalities and consequences of an attack. We describe our experience in running an educational activity on ethical hacking, which we proposed to computer science and computer engineering students. We organized seminars, outside formal classes, and provided online support on the hands-on part of the training. We delivered different types of exercises and held a final CTF competition. These activities resulted in growing a community of students and researchers interested in cybersecurity, and some of them have formed ZenHack, an official CTF team

Comparing faces: a computational and perceptual study

The problem of extracting distinctive parts from a face is addressed. Rather than examining a priori specified features such as nose, eyes, month or others, the aim here is to extract from a face the most distinguishing or dissimilar parts with respect to another given face, i.e. finding differences between faces. A computational approach, based on log polar patch sampling and evaluation, has been compared with results obtained from a newly designed perceptual test involving 45 people. The results of the comparison confirm the potential of the proposed computational method

Road traffic pollution and childhood leukemia: a nationwide case-control study in Italy

Background The association of childhood leukemia with traffic pollution was considered in a number of studies from 1989 onwards, with results not entirely consistent and little information regarding subtypes. Aim of the study We used the data of the Italian SETIL case-control on childhood leukemia to explore the risk by leukemia subtypes associated to exposure to vehicular traffic. Methods We included in the analyses 648 cases of childhood leukemia (565 Acute lymphoblastic–ALL and 80 Acute non lymphoblastic-AnLL) and 980 controls. Information on traffic exposure was collected from questionnaire interviews and from the geocoding of house addresses, for all periods of life of the children. Results We observed an increase in risk for AnLL, and at a lower extent for ALL, with indicators of exposure to traffic pollutants. In particular, the risk was associated to the report of closeness of the house to traffic lights and to the passage of trucks (OR: 1.76; 95% CI 1.03–3.01 for ALL and 6.35; 95% CI 2.59–15.6 for AnLL). The association was shown also in the analyses limited to AML and in the stratified analyses and in respect to the house in different period of life. Conclusions Results from the SETIL study provide some support to the association of traffic related exposure and risk for AnLL, but at a lesser extent for ALL. Our conclusion highlights the need for leukemia type specific analyses in future studies. Results support the need of controlling exposure from traffic pollution, even if knowledge is not complete

El agua es prioridad: lecciones del proyecto Euroclima

En el marco del proyecto EUROCLIMA financiando por la Unión Europeae implementado por el INTA, se han elaborado talleres participativos de análisis de riesgo en el contexto de cambio climático. Los resultados en Patagonia Norte muestran que el mayor peligro percibido es la escasez de agua; las medidas de adaptación al cambio climático identificadas buscan paliar esta falta.Fil: de Pascuale Bovi, Juan Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Ocariz, María Paula. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Fernandez, Manuela Teresa. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Bidinost, Franca. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Conti, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio. Universidad Nacional de Río Negro. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio; ArgentinaFil: Hara, Sofía María. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Madariaga, Marta Cecilia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Gallo, Leonardo Ariel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Deluchi, Saúl Gastón. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Laborda, Luciana. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Alvarez, Valeria Esther. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: El Mujtar, Verónica Andrea. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Gallardo, Alejandra. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Agencia de Extensión Rural Zapala; ArgentinaFil: Navedo, Rodrigo Manuel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Agencia de Extensión Rural Zapala; ArgentinaFil: Gizzi, Giuliana. Centro Formación Profesional N°8; ArgentinaFil: Garabito, Fernando Gastón. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Agencia de Extensión Rural Picún Leufú; ArgentinaFil: Conterno, Cecilia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Agencia de Extensión Rural Picún Leufú; ArgentinaFil: Maldonado, María Inés. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Agencia de Extensión Rural Ing. Jacobacci; ArgentinaFil: Michel, Carolina Lara. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio. Universidad Nacional de Río Negro. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio; ArgentinaFil: Rovaretti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio. Universidad Nacional de Río Negro. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio; ArgentinaFil: Fantozzi, Anabella Giselle. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Odeon, Maria Mercedes. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Duprez, Juan Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Lagorio, Paula Agustina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Barth, Iris Dagmar. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Tittonell, Pablo Adrián. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentin

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Understanding iconic image-based face biometrics

In the last decade, many recognition and authentication systems based on biometric mesaurements have been proposed. Still algorithms based on face images are quite appealing for the possibility to easy adapt and taylor a system to many application domains. A system for personal identity verification and also recognition is presented. The core engine is a standard correlation-based matcher performed on iconic representations of face images. Two data sets are used to validate the performances of the whole system (from data acquisition to recognition): the former is a standard “academic” database (with known acquisition parameters) similar to the FERET image set, the latter is an “industrial” data set acquired in a real application scenario. Through standard statistical tests of the recognition results obtained from the two data sets the actual physical limits of the pattern matcher are clearly shown. Successively also other aspects are taken into account, related to the feature space, allowing to greatly improve the system performance reaching almost 100% correct recognition. Several hints for the development of new techniques for identity verification are also suggested