Face/Off

Ilia, Panagiotis; Polakis, Iasonas; Αθανασόπουλος, Ηλίας; Maggi, Federico; Ioannidis, Sotiris

doi:10.1145/2810103.2813603

Cited by 103 publications

(39 citation statements)

References 22 publications

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“…In this approach, part of the photo is hidden, for example by blurring, pixelating or distorting a person's face to avoid identification. Blurring is the most commonly used and widely studied approach to controlling photo content disclosure (e.g., Ilia, Polakis, Athanasopoulos, Maggi, & Ioannidis, 2015;Li, Li, & Gao, 2016).…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Vishwamitra

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Photo sharing on online social networks (OSNs) can cause privacy issues. Face blurring is one strategy to increase privacy while still allowing users to share photos. To explore the potential blurring has as a privacy-enhancing technology for OSN photos, we conducted an online experiment with 47 participants to evaluate the effectiveness of face blurring compared to the original photo (as-is), and users’ experience (satisfaction, information sufficiency, enjoyment, social presence, and filter likeability). Users’ experience ratings for face blurring were positive, indicating blurring may be an acceptable way to modify photos from the users’ perspective. However, from a privacy-enhancement perspective, while face blurring may be useful in some situations, such as those where the person in the photo is unknown to the viewer, in other cases, such as in an OSN where the person in the image is known to the viewer, face blurring does not provide privacy protection.

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Section: Introductionmentioning

confidence: 99%

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Vishwamitra

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

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“…Researchers also provided public awareness of privacy risks associated with images shared online [Henne et al 2013;Xu et al 2015]. Along this line, several works are carried out to study users' privacy concerns in social networks, privacy decisions about sharing resources, and the risk associated with them [Ghazinour et al 2013;Gross and Acquisti 2005;Ilia et al 2015;Krishnamurthy and Wills 2008;Parra-Arnau et al 2014;Parra-Arnau et al 2012;Simpson 2008].…”

Section: Related Workmentioning

confidence: 99%

Image Privacy Prediction Using Deep Neural Networks

Tonge

Caragea

2020

ACM Trans. Web

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Images today are increasingly shared online on social networking sites such as Facebook, Flickr, Foursquare, and Instagram. Image sharing occurs not only within a group of friends but also more and more outside a user's social circles for purposes of social discovery. Despite that current social networking sites allow users to change their privacy preferences, this is often a cumbersome task for the vast majority of users on the Web, who face difficulties in assigning and managing privacy settings. When these privacy settings are used inappropriately, online image sharing can potentially lead to unwanted disclosures and privacy violations. Thus, automatically predicting images' privacy to warn users about private or sensitive content before uploading these images on social networking sites has become a necessity in our current interconnected world.In this paper, we explore learning models to automatically predict appropriate images' privacy as private or public using carefully identified image-specific features. We study deep visual semantic features that are derived from various layers of Convolutional Neural Networks (CNNs) as well as textual features such as user tags and deep tags generated from deep CNNs. Particularly, we extract deep (visual and tag) features from four pre-trained CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and ResNet, and compare their performance for image privacy prediction. Among all four networks, we observe that ResNet produces the best feature representations for this task. We also fine-tune the pre-trained CNN architectures on our privacy dataset and compare their performance with the models trained on pre-trained features. The results show that even though the overall performance obtained using the fine-tuned networks is comparable to that of pre-trained networks, the fine-tuned networks provide an improved performance for the private class as compared to models trained on the pre-trained features. Results of our experiments on a Flickr dataset of over thirty thousand images show that the learning models trained on features extracted from ResNet outperform the state-of-the-art models for image privacy prediction. We further investigate the combination of user tags and deep tags derived from CNN architectures using two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. We compare these models with the models trained on ResNet visual features obtained for privacy prediction. Our results show that even though the models trained

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“…The exploration of these risks was investigated by [3]. Studies have provided third-party applications that will help reduce the amount of visual privacy leaks until social media platforms employ further action [11,15,22,23].…”

Section: Previous Literaturementioning

confidence: 99%

Social Media and the Scourge of Visual Privacy

2020

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Online privacy has become immensely important with the growth of technology and the expansion of communication. Social Media Networks have risen to the forefront of current communication trends. With the current trends in social media, the question now becomes how can we actively protect ourselves on these platforms? Users of social media networks share billions of images a day. Whether intentional or unintentional, users tend to share private information within these images. In this study, we investigate (1) the users’ perspective of privacy, (2) pervasiveness of privacy leaks on Twitter, and (3) the threats and dangers on these platforms. In this study, we incorporate techniques such as text analysis, analysis of variance, and crowdsourcing to process the data received from these sources. Based on the results, the participants’ definitions of privacy showed overlap regardless of age or gender identity. After looking at the survey results, most female participants displayed a heightened fear of dangers on social media networks because of threats in the following areas: assets and identity. When the participants were asked to rank the threats on social media, they showed a high concern for burglary and kidnapping. We find that participants need more education about the threats of visual content and how these privacy leaks can lead to physical, mental, and emotional danger.

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Face/Off

Cited by 103 publications

References 22 publications

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Effectiveness and Users’ Experience of Face Blurring as a Privacy Protection for Sharing Photos via Online Social Networks

Image Privacy Prediction Using Deep Neural Networks

Social Media and the Scourge of Visual Privacy

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