Pub Date : 2020-04-07DOI: 10.1186/s13635-021-00127-0
Stefano Calzavara, C. Lucchese, Federico Marcuzzi, S. Orlando
{"title":"Feature partitioning for robust tree ensembles and their certification in adversarial scenarios","authors":"Stefano Calzavara, C. Lucchese, Federico Marcuzzi, S. Orlando","doi":"10.1186/s13635-021-00127-0","DOIUrl":"https://doi.org/10.1186/s13635-021-00127-0","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2020-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48949808","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 : 2020-04-07DOI: 10.1186/s13635-020-00105-y
Angelo Sotgiu, Ambra Demontis, Marco Melis, B. Biggio, G. Fumera, Xiaoyi Feng, F. Roli
{"title":"Deep neural rejection against adversarial examples","authors":"Angelo Sotgiu, Ambra Demontis, Marco Melis, B. Biggio, G. Fumera, Xiaoyi Feng, F. Roli","doi":"10.1186/s13635-020-00105-y","DOIUrl":"https://doi.org/10.1186/s13635-020-00105-y","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2020 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2020-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-00105-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65684020","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 : 2020-03-17DOI: 10.1186/s13635-020-00103-0
Elakkiya Ellavarason, Richard Guest, Farzin Deravi
User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. The evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.
{"title":"Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device","authors":"Elakkiya Ellavarason, Richard Guest, Farzin Deravi","doi":"10.1186/s13635-020-00103-0","DOIUrl":"https://doi.org/10.1186/s13635-020-00103-0","url":null,"abstract":"User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. The evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"8 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2020-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138536922","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 : 2020-03-12DOI: 10.1186/s13635-020-0102-6
C. Wright, D. Stewart
{"title":"Understanding visual lip-based biometric authentication for mobile devices","authors":"C. Wright, D. Stewart","doi":"10.1186/s13635-020-0102-6","DOIUrl":"https://doi.org/10.1186/s13635-020-0102-6","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2020 1","pages":"1-16"},"PeriodicalIF":3.6,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-0102-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43745996","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 : 2020-02-21DOI: 10.1186/s13635-020-0100-8
N. Whiskerd, Nicklas Körtge, Kris Jürgens, Kevin Lamshöft, Salatiel Ezennaya-Gomez, C. Vielhauer, J. Dittmann, M. Hildebrandt
{"title":"Keystroke biometrics in the encrypted domain: a first study on search suggestion functions of web search engines","authors":"N. Whiskerd, Nicklas Körtge, Kris Jürgens, Kevin Lamshöft, Salatiel Ezennaya-Gomez, C. Vielhauer, J. Dittmann, M. Hildebrandt","doi":"10.1186/s13635-020-0100-8","DOIUrl":"https://doi.org/10.1186/s13635-020-0100-8","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-0100-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42762486","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 : 2020-01-17DOI: 10.1186/s13635-020-0101-7
D. Cozzolino, Francesco Marra, Diego Gragnaniello, G. Poggi, L. Verdoliva
{"title":"Combining PRNU and noiseprint for robust and efficient device source identification","authors":"D. Cozzolino, Francesco Marra, Diego Gragnaniello, G. Poggi, L. Verdoliva","doi":"10.1186/s13635-020-0101-7","DOIUrl":"https://doi.org/10.1186/s13635-020-0101-7","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-0101-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44718634","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 : 2020-01-01Epub Date: 2020-06-01DOI: 10.1186/s13635-020-00106-x
Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy
In recent years, classification techniques based on deep neural networks (DNN) were widely used in many fields such as computer vision, natural language processing, and self-driving cars. However, the vulnerability of the DNN-based classification systems to adversarial attacks questions their usage in many critical applications. Therefore, the development of robust DNN-based classifiers is a critical point for the future deployment of these methods. Not less important issue is understanding of the mechanisms behind this vulnerability. Additionally, it is not completely clear how to link machine learning with cryptography to create an information advantage of the defender over the attacker. In this paper, we propose a key-based diversified aggregation (KDA) mechanism as a defense strategy in a gray- and black-box scenario. KDA assumes that the attacker (i) knows the architecture of classifier and the used defense strategy, (ii) has an access to the training data set, but (iii) does not know a secret key and does not have access to the internal states of the system. The robustness of the system is achieved by a specially designed key-based randomization. The proposed randomization prevents the gradients' back propagation and restricts the attacker to create a "bypass" system. The randomization is performed simultaneously in several channels. Each channel introduces its own randomization in a special transform domain. The sharing of a secret key between the training and test stages creates an information advantage to the defender. Finally, the aggregation of soft outputs from each channel stabilizes the results and increases the reliability of the final score. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against state-of-the-art gradient-based gray-box transferability attacks and the non-gradient-based black-box attacks (The results reported in this paper have been partially presented in CVPR 2019 (Taran et al., Defending against adversarial attacks by randomized diversification, 2019) & ICIP 2019 (Taran et al., Robustification of deep net classifiers by key-based diversified aggregation with pre-filtering, 2019)).
{"title":"Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation.","authors":"Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy","doi":"10.1186/s13635-020-00106-x","DOIUrl":"https://doi.org/10.1186/s13635-020-00106-x","url":null,"abstract":"<p><p>In recent years, classification techniques based on deep neural networks (DNN) were widely used in many fields such as computer vision, natural language processing, and self-driving cars. However, the vulnerability of the DNN-based classification systems to adversarial attacks questions their usage in many critical applications. Therefore, the development of robust DNN-based classifiers is a critical point for the future deployment of these methods. Not less important issue is understanding of the mechanisms behind this vulnerability. Additionally, it is not completely clear how to link machine learning with cryptography to create an information advantage of the defender over the attacker. In this paper, we propose a key-based diversified aggregation (KDA) mechanism as a defense strategy in a gray- and black-box scenario. KDA assumes that the attacker (i) knows the architecture of classifier and the used defense strategy, (ii) has an access to the training data set, but (iii) does not know a secret key and does not have access to the internal states of the system. The robustness of the system is achieved by a specially designed key-based randomization. The proposed randomization prevents the gradients' back propagation and restricts the attacker to create a \"bypass\" system. The randomization is performed simultaneously in several channels. Each channel introduces its own randomization in a special transform domain. The sharing of a secret key between the training and test stages creates an information advantage to the defender. Finally, the aggregation of soft outputs from each channel stabilizes the results and increases the reliability of the final score. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against state-of-the-art gradient-based gray-box transferability attacks and the non-gradient-based black-box attacks (The results reported in this paper have been partially presented in CVPR 2019 (Taran et al., Defending against adversarial attacks by randomized diversification, 2019) & ICIP 2019 (Taran et al., Robustification of deep net classifiers by key-based diversified aggregation with pre-filtering, 2019)).</p>","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2020 1","pages":"10"},"PeriodicalIF":3.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-00106-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38177556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-29DOI: 10.1186/s13635-019-0099-x
J. Buchmann, Matthias Geihs, K. Hamacher, S. Katzenbeisser, S. Stammler
{"title":"Long-term integrity protection of genomic data","authors":"J. Buchmann, Matthias Geihs, K. Hamacher, S. Katzenbeisser, S. Stammler","doi":"10.1186/s13635-019-0099-x","DOIUrl":"https://doi.org/10.1186/s13635-019-0099-x","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-019-0099-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47723221","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 : 2019-09-10DOI: 10.1186/s13635-020-00109-8
Xinyi Ding, Zohreh Raziei, Eric C. Larson, E. Olinick, P. Krueger, Michael Hahsler
{"title":"Swapped face detection using deep learning and subjective assessment","authors":"Xinyi Ding, Zohreh Raziei, Eric C. Larson, E. Olinick, P. Krueger, Michael Hahsler","doi":"10.1186/s13635-020-00109-8","DOIUrl":"https://doi.org/10.1186/s13635-020-00109-8","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2020 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13635-020-00109-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48733952","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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