EURASIP Journal on Information Security最新文献_第5页

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes CAPTCHA码安全分析的人工智能方法

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-12-12 DOI: 10.1186/s13635-022-00134-9

Nghia Dinh, L. Ogiela

引用次数: 4

Behavior-based user authentication on mobile devices in various usage contexts 在各种使用环境下移动设备上基于行为的用户身份验证

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-09-16 DOI: 10.1186/s13635-022-00132-x

D. Progonov, Valentyna Cherniakova, Pavlo Kolesnichenko, Andriy Oliynyk

引用次数: 2

Presentation attack detection and biometric recognition in a challenge-response formalism 挑战-响应形式化中的呈现攻击检测和生物特征识别

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-09-05 DOI: 10.1186/s13635-022-00131-y

E. Haasnoot, L. Spreeuwers, R. Veldhuis

引用次数: 1

Robust video steganography for social media sharing based on principal component analysis 基于主成分分析的社交媒体共享鲁棒视频隐写

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-06-20 DOI: 10.1186/s13635-022-00130-z

Pingan Fan, Hong Zhang, Xianfeng Zhao

引用次数: 3

On the methodology of fingerprint template protection schemes conception : meditations on the reliability 指纹模板保护方案构想的方法学:对可靠性的思考

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-03-25 DOI: 10.1186/s13635-022-00129-6

A. Lahmidi, Chouaib Moujahdi, K. Minaoui, M. Rziza

引用次数: 6

DIPPAS: a deep image prior PRNU anonymization scheme DIPPAS:一种深度图像先验PRNU匿名化方案

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-02-14 DOI: 10.1186/s13635-022-00128-7

Picetti, Francesco, Mandelli, Sara, Bestagini, Paolo, Lipari, Vincenzo, Tubaro, Stefano

{"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}

引用次数: 9

Secure machine learning against adversarial samples at test time 在测试时针对对抗性样本进行安全的机器学习

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-01-12 DOI: 10.1186/s13635-021-00125-2

Jing Lin, L. Njilla, Kaiqi Xiong

引用次数: 7

"Alexa, What's a Phishing Email?": Training users to spot phishing emails using a voice assistant. “Alexa，什么是网络钓鱼邮件?”:培训用户使用语音助手识别网络钓鱼邮件。

IF 3.6

EURASIP Journal on Information Security Pub Date : 2022-01-01 Epub Date: 2022-11-22 DOI: 10.1186/s13635-022-00133-w

Filipo Sharevski, Peter Jachim

{"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":"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.","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}

引用次数: 1

Image life trails based on contrast reduction models for face counter-spoofing 基于对比度降低模型的人脸反欺骗图像生命轨迹

IF 3.6

EURASIP Journal on Information Security Pub Date : 2021-12-29 DOI: 10.1186/s13635-022-00135-8

Balaji Rao Katika, K. Karthik

引用次数: 0

Robust video steganography for social media sharing based on principal component analysis 基于主成分分析的社交媒体共享鲁棒视频隐写

IF 3.6

EURASIP Journal on Information Security Pub Date : 2021-12-29 DOI: 10.21203/rs.3.rs-1109366/v1

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":"10.21203/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}

引用次数: 1