Pub Date : 2022-12-17DOI: 10.1186/s13635-022-00136-7
Binh Le Thanh Thai, Hidema Tanaka, Kohtaro Watanabe
{"title":"Improved scheme and evaluation method for progressive visual cryptography","authors":"Binh Le Thanh Thai, Hidema Tanaka, Kohtaro Watanabe","doi":"10.1186/s13635-022-00136-7","DOIUrl":"https://doi.org/10.1186/s13635-022-00136-7","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48698303","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 : 2022-12-12DOI: 10.1186/s13635-022-00134-9
Nghia Dinh, L. Ogiela
{"title":"Human-artificial intelligence approaches for secure analysis in CAPTCHA codes","authors":"Nghia Dinh, L. Ogiela","doi":"10.1186/s13635-022-00134-9","DOIUrl":"https://doi.org/10.1186/s13635-022-00134-9","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42909518","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 : 2022-09-16DOI: 10.1186/s13635-022-00132-x
D. Progonov, Valentyna Cherniakova, Pavlo Kolesnichenko, Andriy Oliynyk
{"title":"Behavior-based user authentication on mobile devices in various usage contexts","authors":"D. Progonov, Valentyna Cherniakova, Pavlo Kolesnichenko, Andriy Oliynyk","doi":"10.1186/s13635-022-00132-x","DOIUrl":"https://doi.org/10.1186/s13635-022-00132-x","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46883933","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 : 2022-09-05DOI: 10.1186/s13635-022-00131-y
E. Haasnoot, L. Spreeuwers, R. Veldhuis
{"title":"Presentation attack detection and biometric recognition in a challenge-response formalism","authors":"E. Haasnoot, L. Spreeuwers, R. Veldhuis","doi":"10.1186/s13635-022-00131-y","DOIUrl":"https://doi.org/10.1186/s13635-022-00131-y","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65684274","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 : 2022-06-20DOI: 10.1186/s13635-022-00130-z
Pingan Fan, Hong Zhang, Xianfeng Zhao
{"title":"Robust video steganography for social media sharing based on principal component analysis","authors":"Pingan Fan, Hong Zhang, Xianfeng Zhao","doi":"10.1186/s13635-022-00130-z","DOIUrl":"https://doi.org/10.1186/s13635-022-00130-z","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41297171","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 : 2022-03-25DOI: 10.1186/s13635-022-00129-6
A. Lahmidi, Chouaib Moujahdi, K. Minaoui, M. Rziza
{"title":"On the methodology of fingerprint template protection schemes conception : meditations on the reliability","authors":"A. Lahmidi, Chouaib Moujahdi, K. Minaoui, M. Rziza","doi":"10.1186/s13635-022-00129-6","DOIUrl":"https://doi.org/10.1186/s13635-022-00129-6","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65684211","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}
Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counterpart is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the photo response non-uniformity (PRNU), a noise pattern left by the device on the acquired images. In this paper, we devise a methodology for suppressing such a trace from natural images without a significant impact on image quality. Expressly, we turn PRNU anonymization into the combination of a global optimization problem in a deep image prior (DIP) framework followed by local post-processing operations. In a nutshell, a convolutional neural network (CNN) acts as a generator and iteratively returns several images with attenuated PRNU traces. By exploiting straightforward local post-processing and assembly on these images, we produce a final image that is anonymized with respect to the source PRNU, still maintaining high visual quality. With respect to widely adopted deep learning paradigms, the used CNN is not trained on a set of input-target pairs of images. Instead, it is optimized to reconstruct output images from the original image under analysis itself. This makes the approach particularly suitable in scenarios where large heterogeneous databases are analyzed. Moreover, it prevents any problem due to the lack of generalization. Through numerical examples on publicly available datasets, we prove our methodology to be effective compared to state-of-the-art techniques.
{"title":"DIPPAS: a deep image prior PRNU anonymization scheme","authors":"Picetti, Francesco, Mandelli, Sara, Bestagini, Paolo, Lipari, Vincenzo, Tubaro, Stefano","doi":"10.1186/s13635-022-00128-7","DOIUrl":"https://doi.org/10.1186/s13635-022-00128-7","url":null,"abstract":"Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counterpart is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the photo response non-uniformity (PRNU), a noise pattern left by the device on the acquired images. In this paper, we devise a methodology for suppressing such a trace from natural images without a significant impact on image quality. Expressly, we turn PRNU anonymization into the combination of a global optimization problem in a deep image prior (DIP) framework followed by local post-processing operations. In a nutshell, a convolutional neural network (CNN) acts as a generator and iteratively returns several images with attenuated PRNU traces. By exploiting straightforward local post-processing and assembly on these images, we produce a final image that is anonymized with respect to the source PRNU, still maintaining high visual quality. With respect to widely adopted deep learning paradigms, the used CNN is not trained on a set of input-target pairs of images. Instead, it is optimized to reconstruct output images from the original image under analysis itself. This makes the approach particularly suitable in scenarios where large heterogeneous databases are analyzed. Moreover, it prevents any problem due to the lack of generalization. Through numerical examples on publicly available datasets, we prove our methodology to be effective compared to state-of-the-art techniques.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138536925","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 : 2022-01-12DOI: 10.1186/s13635-021-00125-2
Jing Lin, L. Njilla, Kaiqi Xiong
{"title":"Secure machine learning against adversarial samples at test time","authors":"Jing Lin, L. Njilla, Kaiqi Xiong","doi":"10.1186/s13635-021-00125-2","DOIUrl":"https://doi.org/10.1186/s13635-021-00125-2","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49516167","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 : 2022-01-01Epub Date: 2022-11-22DOI: 10.1186/s13635-022-00133-w
Filipo Sharevski, Peter Jachim
This paper reports the findings from an empirical study investigating the effectiveness of using intelligent voice assistants, Amazon Alexa in our case, to deliver a phishing training to users. Because intelligent voice assistants can hardly utilize visual cues but provide for convenient interaction with users, we developed an interaction-based phishing training focused on the principles of persuasion with examples on how to look for them in phishing emails. To test the effectiveness of this training, we conducted a between-subject study where 120 participants were randomly assigned in three groups: no training, interaction-based training with Alexa, and a facts-and-advice training and assessed a vignette of 28 emails. The results show that the participants in the interaction-based group statistically outperformed the others when detecting phishing emails that employed the following persuasion principles (and/or combinations of): authority, authority/scarcity, commitment, commitment/liking, and scarcity/liking. The paper discusses the implication of this result for future phishing training and anti-phishing efforts.
{"title":"\"Alexa, What's a Phishing Email?\": Training users to spot phishing emails using a voice assistant.","authors":"Filipo Sharevski, Peter Jachim","doi":"10.1186/s13635-022-00133-w","DOIUrl":"https://doi.org/10.1186/s13635-022-00133-w","url":null,"abstract":"<p><p>This paper reports the findings from an empirical study investigating the effectiveness of using intelligent voice assistants, Amazon Alexa in our case, to deliver a phishing training to users. Because intelligent voice assistants can hardly utilize visual cues but provide for convenient interaction with users, we developed an <i>interaction-based phishing training</i> focused on the principles of persuasion with examples on how to look for them in phishing emails. To test the effectiveness of this training, we conducted a between-subject study where 120 participants were randomly assigned in three groups: no training, interaction-based training with Alexa, and a facts-and-advice training and assessed a vignette of 28 emails. The results show that the participants in the interaction-based group statistically outperformed the others when detecting phishing emails that employed the following persuasion principles (and/or combinations of): authority, authority/scarcity, commitment, commitment/liking, and scarcity/liking. The paper discusses the implication of this result for future phishing training and anti-phishing efforts.</p>","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35208957","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 : 2021-12-29DOI: 10.1186/s13635-022-00135-8
Balaji Rao Katika, K. Karthik
{"title":"Image life trails based on contrast reduction models for face counter-spoofing","authors":"Balaji Rao Katika, K. Karthik","doi":"10.1186/s13635-022-00135-8","DOIUrl":"https://doi.org/10.1186/s13635-022-00135-8","url":null,"abstract":"","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43905356","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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