The Experts below are selected from a list of 4131 Experts worldwide ranked by ideXlab platform
Jason Hong - One of the best experts on this subject based on the ideXlab platform.
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the role of social influence in Security Feature adoption
Conference on Computer Supported Cooperative Work, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
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CSCW - The Role of Social Influence in Security Feature Adoption
Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing - CSCW '15, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
Adam D I Kramer - One of the best experts on this subject based on the ideXlab platform.
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the role of social influence in Security Feature adoption
Conference on Computer Supported Cooperative Work, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
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CSCW - The Role of Social Influence in Security Feature Adoption
Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing - CSCW '15, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
Laura Dabbish - One of the best experts on this subject based on the ideXlab platform.
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the role of social influence in Security Feature adoption
Conference on Computer Supported Cooperative Work, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
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CSCW - The Role of Social Influence in Security Feature Adoption
Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing - CSCW '15, 2015Co-Authors: Adam D I Kramer, Laura Dabbish, Jason HongAbstract:Social influence is key in technology adoption, but its role in Security-Feature adoption is unique and remains unclear. Here, we analyzed how three Facebook Security Features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a Security Feature, but its effect varies based on the observability of the Feature, the current Feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those Feature-adopting friends originate. Curiously, there may be a threshold higher than which having more Security Feature adopting friends predicts for higher adoption likelihood, but below which having more Feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a Feature-Features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.
Jean-marc Ogier - One of the best experts on this subject based on the ideXlab platform.
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ICDAR - Hiding Security Feature Into Text Content for Securing Documents Using Generated Font
2019 International Conference on Document Analysis and Recognition (ICDAR), 2019Co-Authors: Vinh Loc Cu, Jean-christophe Burie, Jean-marc OgierAbstract:Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a Security Feature or secret information within the document. In order to hide the Security Feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the Features of character's skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to state-of-the-art approaches.
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Hiding Security Feature Into Text Content for Securing Documents Using Generated Font
2019 International Conference on Document Analysis and Recognition (ICDAR), 2019Co-Authors: Vinh Loc Cu, Jean-christophe Burie, Jean-marc OgierAbstract:Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a Security Feature or secret information within the document. In order to hide the Security Feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the Features of character's skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to state-of-the-art approaches.
Vinh Loc Cu - One of the best experts on this subject based on the ideXlab platform.
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ICDAR - Hiding Security Feature Into Text Content for Securing Documents Using Generated Font
2019 International Conference on Document Analysis and Recognition (ICDAR), 2019Co-Authors: Vinh Loc Cu, Jean-christophe Burie, Jean-marc OgierAbstract:Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a Security Feature or secret information within the document. In order to hide the Security Feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the Features of character's skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to state-of-the-art approaches.
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Hiding Security Feature Into Text Content for Securing Documents Using Generated Font
2019 International Conference on Document Analysis and Recognition (ICDAR), 2019Co-Authors: Vinh Loc Cu, Jean-christophe Burie, Jean-marc OgierAbstract:Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a Security Feature or secret information within the document. In order to hide the Security Feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the Features of character's skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to state-of-the-art approaches.
