TY - JOUR
T1 - Toward automated online photo privacy
AU - Squicciarini, Anna
AU - Caragea, Cornelia
AU - Balakavi, Rahul
N1 - Funding Information:
This work is supported by the National Science Foundation, under grant 1421776 and grant 1421970.
PY - 2017/3
Y1 - 2017/3
N2 - Online photo sharing is an increasingly popular activity for Internet users. More and more users are now constantly sharing their images in various social media, from social networking sites to online communities, blogs, and content sharing sites. In this article, we present an extensive study exploring privacy and sharing needs of users' uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. Our study investigates both visual and textual features of images for privacy classification. We consider both basic image-specific features, commonly used for image processing, as well as more sophisticated and abstract visual features. Additionally, we include a visual representation of the sentiment evoked by images. To our knowledge, sentiment has never been used in the context of image classification for privacy purposes. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users' images. We consider both the case of binary privacy settings (i.e., public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve high accuracy, especially when high-quality tags are available.
AB - Online photo sharing is an increasingly popular activity for Internet users. More and more users are now constantly sharing their images in various social media, from social networking sites to online communities, blogs, and content sharing sites. In this article, we present an extensive study exploring privacy and sharing needs of users' uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. Our study investigates both visual and textual features of images for privacy classification. We consider both basic image-specific features, commonly used for image processing, as well as more sophisticated and abstract visual features. Additionally, we include a visual representation of the sentiment evoked by images. To our knowledge, sentiment has never been used in the context of image classification for privacy purposes. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users' images. We consider both the case of binary privacy settings (i.e., public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve high accuracy, especially when high-quality tags are available.
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U2 - 10.1145/2983644
DO - 10.1145/2983644
M3 - Article
AN - SCOPUS:85017191949
SN - 1559-1131
VL - 11
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
IS - 1
M1 - 2
ER -