Sanghyun Ahn, University of Seoul Amir H. Alavi, University of Missouri Ladjel Bellatreche, LIAS/ENSMA Athman Bouguettaya, The University of Sydney Stephane Bressan, National University of Singapore K. Selcuk Candan, Arizona State University Tru Cao, Ho Chi Minh City University of Technology Songcan Chen, Nanjing University of Aeronautics & Astronautics Hong Chen, Renmin University of China Heeryon Cho, Kookmin University Soo-Mi Choi, Sejong University Mi-Jung Choi, Kangwon National University Hoon Choi, Chungnam National University Jaegul Choo, Korea University Soon Ae Chun, City University of New York Shifei Ding, China University of Mining and Technology Gill Dobbie, The University of Auckland Koji Eguchi, Hiroshima University Sameh Elnikety, Microsoft Young Ik Eom, Sungkyunkwan University Sergio Flesca, University of Calabria Zhipeng Gao, Beijing University of Posts and Telecommunications Wei Gao, Nanjing University Hong Gao, Harbin Institute of Technology Xin Geng, Southeast University Chen Gong, Shanghai Jiao Tong University Hyoil Han, Illinois State University Kenji Hatano, Doshisha University Kazumasa Horie, University of Tsukuba Wen Hua, The University of Queensland Seung-Won Hwang, Yonsei University Eenjun Hwang, Korea University Hyeonseung Im, Kangwon National University Md. Saiful Islam, Griffith University Young-Seob Jeong, SoonChunHyang University Seong-Ho Jeong, Hankuk University of Foreign Studies Xiaolong Jin, Chinese Academy of Sciences
{"title":"Program Committee","authors":"Pengcheng Zhang, Junhua Ding, Xiaobing Sun, Mingyue Jiang","doi":"10.1109/eisic49498.2019.9108778","DOIUrl":"https://doi.org/10.1109/eisic49498.2019.9108778","url":null,"abstract":"Sanghyun Ahn, University of Seoul Amir H. Alavi, University of Missouri Ladjel Bellatreche, LIAS/ENSMA Athman Bouguettaya, The University of Sydney Stephane Bressan, National University of Singapore K. Selcuk Candan, Arizona State University Tru Cao, Ho Chi Minh City University of Technology Songcan Chen, Nanjing University of Aeronautics & Astronautics Hong Chen, Renmin University of China Heeryon Cho, Kookmin University Soo-Mi Choi, Sejong University Mi-Jung Choi, Kangwon National University Hoon Choi, Chungnam National University Jaegul Choo, Korea University Soon Ae Chun, City University of New York Shifei Ding, China University of Mining and Technology Gill Dobbie, The University of Auckland Koji Eguchi, Hiroshima University Sameh Elnikety, Microsoft Young Ik Eom, Sungkyunkwan University Sergio Flesca, University of Calabria Zhipeng Gao, Beijing University of Posts and Telecommunications Wei Gao, Nanjing University Hong Gao, Harbin Institute of Technology Xin Geng, Southeast University Chen Gong, Shanghai Jiao Tong University Hyoil Han, Illinois State University Kenji Hatano, Doshisha University Kazumasa Horie, University of Tsukuba Wen Hua, The University of Queensland Seung-Won Hwang, Yonsei University Eenjun Hwang, Korea University Hyeonseung Im, Kangwon National University Md. Saiful Islam, Griffith University Young-Seob Jeong, SoonChunHyang University Seong-Ho Jeong, Hankuk University of Foreign Studies Xiaolong Jin, Chinese Academy of Sciences","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108780
A. Garbuz, A. Epishkina, K. Kogos
Nowadays, smartphones are used for getting access to sensitive and private data. As a result, we need an authentication system that will provide smartphones with additional security and at the same time will not cause annoyance to users. Existing authentication mechanisms provide just a one-time user verification and do not perform re-authentication in the process of further interaction. In this paper, we present a continuous user authentication system based on user's interaction with the touchscreen in conjunction with micromovements, performed by smartphones at the same time. We consider two of the most common types of gestures performed by users (vertical swipes up and down, and taps). The novelty of our approach is that swipes and taps are both analyzed to provide continuous authentication. Swipes are informative gestures, while taps are the most common gestures. This way, we aim to reduce the time of impostors' detection. The proposed scheme collects data from the touchscreen and multiple 3-dimensional sensors integrated in all modern smartphones. We use One-Class Support Vector Machine (OSVM) algorithm to get a model of a legitimate user. The obtained results show that the proposed scheme of continuous authentication can improve smartphone security.
如今，智能手机被用来获取敏感和私人数据。因此，我们需要一种认证系统，既能为智能手机提供额外的安全性，同时又不会给用户带来烦恼。现有的身份验证机制只提供一次性的用户验证，而不会在进一步交互的过程中执行重新身份验证。在本文中，我们提出了一种基于用户与触摸屏交互并结合微动作的连续用户认证系统，该系统同时由智能手机执行。我们考虑用户执行的两种最常见的手势类型(上下垂直滑动和轻击)。我们的方法的新颖之处在于，滑动和点击都被分析以提供连续的身份验证。滑动是信息丰富的手势，而轻击是最常见的手势。这样，我们的目标是减少检测冒名顶替者的时间。该方案从所有现代智能手机中集成的触摸屏和多个三维传感器收集数据。我们使用单类支持向量机(One-Class Support Vector Machine, OSVM)算法得到合法用户的模型。实验结果表明，提出的连续认证方案可以提高智能手机的安全性。
{"title":"Continuous Authentication of Smartphone Users via Swipes and Taps Analysis","authors":"A. Garbuz, A. Epishkina, K. Kogos","doi":"10.1109/EISIC49498.2019.9108780","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108780","url":null,"abstract":"Nowadays, smartphones are used for getting access to sensitive and private data. As a result, we need an authentication system that will provide smartphones with additional security and at the same time will not cause annoyance to users. Existing authentication mechanisms provide just a one-time user verification and do not perform re-authentication in the process of further interaction. In this paper, we present a continuous user authentication system based on user's interaction with the touchscreen in conjunction with micromovements, performed by smartphones at the same time. We consider two of the most common types of gestures performed by users (vertical swipes up and down, and taps). The novelty of our approach is that swipes and taps are both analyzed to provide continuous authentication. Swipes are informative gestures, while taps are the most common gestures. This way, we aim to reduce the time of impostors' detection. The proposed scheme collects data from the touchscreen and multiple 3-dimensional sensors integrated in all modern smartphones. We use One-Class Support Vector Machine (OSVM) algorithm to get a model of a legitimate user. The obtained results show that the proposed scheme of continuous authentication can improve smartphone security.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108871
Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro, Kamer Vishi
In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon. This approach has computational and practical advantages: a set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel; the overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives; finally, according to ensemble theory, the output of the overall system will be robust and reliable even in the presence of weak individual models. We evaluated our system running simulations aimed at assessing the accuracy of individual networks and the whole system. The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.
{"title":"Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks","authors":"Alexander Egiazarov, Vasileios Mavroeidis, Fabio Massimo Zennaro, Kamer Vishi","doi":"10.1109/EISIC49498.2019.9108871","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108871","url":null,"abstract":"In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon. This approach has computational and practical advantages: a set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel; the overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives; finally, according to ensemble theory, the output of the overall system will be robust and reliable even in the presence of weak individual models. We evaluated our system running simulations aimed at assessing the accuracy of individual networks and the whole system. The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/eisic49498.2019.9108881
Fabrizia Bemer
The EIO is an important contribution to the topic of the conference, because security informatics is strictly related to the EIO in the way the transmission occurs between authorities.
EIO对本次会议的主题做出了重要贡献，因为安全信息学在当局之间传输的方式上与EIO严格相关。
{"title":"The Directive 2014/41/UE - The European Investigation Order","authors":"Fabrizia Bemer","doi":"10.1109/eisic49498.2019.9108881","DOIUrl":"https://doi.org/10.1109/eisic49498.2019.9108881","url":null,"abstract":"The EIO is an important contribution to the topic of the conference, because security informatics is strictly related to the EIO in the way the transmission occurs between authorities.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121406119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108873
A. Epishkina, Mikhail Finoshin, K. Kogos, Aleksandra Yazykova
Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via
{"title":"Timing Covert Channels Detection Cases via Machine Learning","authors":"A. Epishkina, Mikhail Finoshin, K. Kogos, Aleksandra Yazykova","doi":"10.1109/EISIC49498.2019.9108873","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108873","url":null,"abstract":"Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108852
Marianela García Lozano, Johan Fernquist
The temptation to influence and sway public opinion most certainly increases with the growth of open online forums where anyone anonymously can express their views and opinions. Since online review sites are a popular venue for opinion influencing attacks, there is a need to automatically identify deceptive posts. The main focus of this work is on automatic identification of deceptive reviews, both positive and negative biased. With this objective, we build a deceptive review SVM based classification model and explore the performance impact of using different feature types (TF-IDF, word2vec, PCFG). Moreover, we study the transferability of trained classification models applied to review data sets of other types of products, and, the classifier robustness, i.e., the accuracy impact, against attacks by stylometry obfuscation trough machine translation. Our findings show that i) we achieve an accuracy of over 90% using different feature types, ii) the trained classification models do not perform well when applied on other data sets containing reviews of different products, and iii) machine translation only slightly impacts the results and can not be used as a viable attack method.
{"title":"Identifying Deceptive Reviews: Feature Exploration, Model Transferability and Classification Attack","authors":"Marianela García Lozano, Johan Fernquist","doi":"10.1109/EISIC49498.2019.9108852","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108852","url":null,"abstract":"The temptation to influence and sway public opinion most certainly increases with the growth of open online forums where anyone anonymously can express their views and opinions. Since online review sites are a popular venue for opinion influencing attacks, there is a need to automatically identify deceptive posts. The main focus of this work is on automatic identification of deceptive reviews, both positive and negative biased. With this objective, we build a deceptive review SVM based classification model and explore the performance impact of using different feature types (TF-IDF, word2vec, PCFG). Moreover, we study the transferability of trained classification models applied to review data sets of other types of products, and, the classifier robustness, i.e., the accuracy impact, against attacks by stylometry obfuscation trough machine translation. Our findings show that i) we achieve an accuracy of over 90% using different feature types, ii) the trained classification models do not perform well when applied on other data sets containing reviews of different products, and iii) machine translation only slightly impacts the results and can not be used as a viable attack method.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117029388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108896
Johan Fernquist, Ola Svenonius, Lisa Kaati, F. Johansson
The last years has witnessed a surge of auto-generated content on social media. While many uses are legitimate, bots have also been deployed in influence operations to manipulate election results, affect public opinion in a desired direction, or to divert attention from a specific event or phenomenon. Today, many approaches exist to automatically identify bot-like behaviour in order to curb illegitimate influence operations. While progress has been made, existing models are exceedingly complex and nontransparent, rendering validation and model testing difficult. We present a transparent and parsimonious method to study influence operations on Twitter. We define nine different attributes that can be used to describe and reason about different characteristics of a Twitter account. The attributes can be used to group accounts that have similar characteristics and the result can be used to identify accounts that are likely to be used to influence public opinion. The method has been tested on a Twitter data set consisting of 66,000 accounts. Clustering the accounts based on the proposed features show promising results for separating between different groups of reference accounts.
{"title":"Extracting Account Attributes for Analyzing Influence on Twitter","authors":"Johan Fernquist, Ola Svenonius, Lisa Kaati, F. Johansson","doi":"10.1109/EISIC49498.2019.9108896","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108896","url":null,"abstract":"The last years has witnessed a surge of auto-generated content on social media. While many uses are legitimate, bots have also been deployed in influence operations to manipulate election results, affect public opinion in a desired direction, or to divert attention from a specific event or phenomenon. Today, many approaches exist to automatically identify bot-like behaviour in order to curb illegitimate influence operations. While progress has been made, existing models are exceedingly complex and nontransparent, rendering validation and model testing difficult. We present a transparent and parsimonious method to study influence operations on Twitter. We define nine different attributes that can be used to describe and reason about different characteristics of a Twitter account. The attributes can be used to group accounts that have similar characteristics and the result can be used to identify accounts that are likely to be used to influence public opinion. The method has been tested on a Twitter data set consisting of 66,000 accounts. Clustering the accounts based on the proposed features show promising results for separating between different groups of reference accounts.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114240993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108787
M. Pic, Gaël Mahfoudi, Anis Trabelsi
Onboarding of new customers is a sensitive task for various services, like Banks who have to follow the Know Your Customer (KYC) rules. Mobile Onboarding Applications or KYC by Streaming are expanding rapidly to provide this capacity at home. Unfortunately, this leaves the authentication tools in the hand of end-users, allowing the attacker to directly tamper the video stream. With the rise of new digital face manipulation technologies, traditional face spoofing attacks such as presentation attacks or replay attacks should not be the only one to be considered. A new kind of face spoofing attacks (i.e. digital face spoofing) needs to be studied carefully. In this paper, we analyze those new kinds of attacks and propose a method to secure identity documents against both the traditional attacks and the new ones.
{"title":"Remote KYC: Attacks and Counter-Measures","authors":"M. Pic, Gaël Mahfoudi, Anis Trabelsi","doi":"10.1109/EISIC49498.2019.9108787","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108787","url":null,"abstract":"Onboarding of new customers is a sensitive task for various services, like Banks who have to follow the Know Your Customer (KYC) rules. Mobile Onboarding Applications or KYC by Streaming are expanding rapidly to provide this capacity at home. Unfortunately, this leaves the authentication tools in the hand of end-users, allowing the attacker to directly tamper the video stream. With the rise of new digital face manipulation technologies, traditional face spoofing attacks such as presentation attacks or replay attacks should not be the only one to be considered. A new kind of face spoofing attacks (i.e. digital face spoofing) needs to be studied carefully. In this paper, we analyze those new kinds of attacks and propose a method to secure identity documents against both the traditional attacks and the new ones.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129178846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108861
Panos Kostakos, Somkiadcharoen Robroo, Bofan Lin, M. Oussalah
Can hotel reviews be used as a proxy for predicting crime hotspots? Domain knowledge indicates that hotels are crime attractors, and therefore, hotel guests might be reliable “human crime sensors”. In order to assess this heuristic, we propose a novel method by mapping actual crime events into hotel reviews from London, using spatial clustering and sentiment feedback. Preliminary findings indicate that sentiment scores from hotel reviews are inversely correlated with crime intensity. Hotels with positive reviews are more likely to be adjacent to crime hotspots, and vice versa. One possible explanation for this counterintuitive finding that the review data are not mapped against specific crime types, and thus the crime data capture mostly police visibility on the site. More research and domain knowledge are needed to establish the strength of hotel reviews as a proxy for crime prediction.
{"title":"Crime Prediction Using Hotel Reviews?","authors":"Panos Kostakos, Somkiadcharoen Robroo, Bofan Lin, M. Oussalah","doi":"10.1109/EISIC49498.2019.9108861","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108861","url":null,"abstract":"Can hotel reviews be used as a proxy for predicting crime hotspots? Domain knowledge indicates that hotels are crime attractors, and therefore, hotel guests might be reliable “human crime sensors”. In order to assess this heuristic, we propose a novel method by mapping actual crime events into hotel reviews from London, using spatial clustering and sentiment feedback. Preliminary findings indicate that sentiment scores from hotel reviews are inversely correlated with crime intensity. Hotels with positive reviews are more likely to be adjacent to crime hotspots, and vice versa. One possible explanation for this counterintuitive finding that the review data are not mapped against specific crime types, and thus the crime data capture mostly police visibility on the site. More research and domain knowledge are needed to establish the strength of hotel reviews as a proxy for crime prediction.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130481333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/EISIC49498.2019.9108870
S. El-Naggar, T. Bourlai
Automated human authentication is becoming increasingly popular on a variety of daily activities, ranging from surveillance to commercial related applications. While there are many biometric modalities that can be used, ear recognition has earned its value if and when available to be captured. Ears demonstrate specific advantages over other competitors in an effort to identify cooperative and non-cooperative individuals in either controlled or challenging environments. The performance of ear recognition systems can be impacted by several factors, including standoff distance, ear pose angle, and ear image quality. While all three factors can degrade ear recognition performance, here we focus on the latter two using real data, and assess the standoff distance factor by synthetically generating blurry and noisy images to simulate longer distance ear images. Thus, in this work we are inspired by various studies in the literature that discuss the how and why challenging biometric images of different modalities impact the associated biometric system recognition. Specifically, we focus on how different ear image distortions and yaw pose angles affect the performance of various deep learning based ear recognition models. Our contributions are threefold. Firstly, we are using challenging ear dataset, with a wide range of yaw pose angles, to evaluate the ear recognition performance of various original ear matching approaches. Secondly, by examining multiple convolutional neural network (CNN) architectures and employing multiple techniques for the learning process, we determine the most efficient CNN - based ear recognition approach. Thirdly, we investigated the impact on performance of a set of ear recognition CNN models in the presence of multiple image degradation factors, including variations of blurriness, additive noise, brightness and contrast.
{"title":"Evaluation of Deep Learning Models for Ear Recognition Against Image Distortions","authors":"S. El-Naggar, T. Bourlai","doi":"10.1109/EISIC49498.2019.9108870","DOIUrl":"https://doi.org/10.1109/EISIC49498.2019.9108870","url":null,"abstract":"Automated human authentication is becoming increasingly popular on a variety of daily activities, ranging from surveillance to commercial related applications. While there are many biometric modalities that can be used, ear recognition has earned its value if and when available to be captured. Ears demonstrate specific advantages over other competitors in an effort to identify cooperative and non-cooperative individuals in either controlled or challenging environments. The performance of ear recognition systems can be impacted by several factors, including standoff distance, ear pose angle, and ear image quality. While all three factors can degrade ear recognition performance, here we focus on the latter two using real data, and assess the standoff distance factor by synthetically generating blurry and noisy images to simulate longer distance ear images. Thus, in this work we are inspired by various studies in the literature that discuss the how and why challenging biometric images of different modalities impact the associated biometric system recognition. Specifically, we focus on how different ear image distortions and yaw pose angles affect the performance of various deep learning based ear recognition models. Our contributions are threefold. Firstly, we are using challenging ear dataset, with a wide range of yaw pose angles, to evaluate the ear recognition performance of various original ear matching approaches. Secondly, by examining multiple convolutional neural network (CNN) architectures and employing multiple techniques for the learning process, we determine the most efficient CNN - based ear recognition approach. Thirdly, we investigated the impact on performance of a set of ear recognition CNN models in the presence of multiple image degradation factors, including variations of blurriness, additive noise, brightness and contrast.","PeriodicalId":117256,"journal":{"name":"2019 European Intelligence and Security Informatics Conference (EISIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114806693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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