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":"2022 1","pages":"1-20"},"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":"2022 1","pages":"1-11"},"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":"49 1","pages":""},"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":"388 1-6","pages":""},"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":"2022 1","pages":""},"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":"20 1","pages":""},"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":"2022 1","pages":"1-15"},"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":"2022 1","pages":"7"},"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":"2023 1","pages":""},"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}
Pub Date : 2021-12-29DOI: 10.21203/rs.3.rs-1109366/v1
Pingan Fan, Hong Zhang, Xianfeng Zhao
Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.
{"title":"Robust video steganography for social media sharing based on principal component analysis","authors":"Pingan Fan, Hong Zhang, Xianfeng Zhao","doi":"10.21203/rs.3.rs-1109366/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-1109366/v1","url":null,"abstract":"Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.","PeriodicalId":46070,"journal":{"name":"EURASIP Journal on Information Security","volume":"2022 1","pages":"1-19"},"PeriodicalIF":3.6,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48386677","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：