Towards privacy-aware photo sharing using mobile phones

Shamma, Sadia; Uddin, Yusuf Sarwar

doi:10.1109/icece.2014.7026919

Cited by 3 publications

(1 citation statement)

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“…Visual-based approaches. Several works used visual features derived from the images' content and showed that they are informative for predicting images' privacy settings [Buschek et al 2015;Dufaux and Ebrahimi 2008;Hu et al 2016;Nakashima et al 2011Nakashima et al , 2012Nakashima et al , 2016Orekondy et al 2018;Shamma and Uddin 2014;Squicciarini et al 2014Squicciarini et al , 2017aTonge and Caragea 2015, 2016Tran et al 2016;von Zezschwitz et al 2016;Wu et al 2018;Yu et al 2017aYu et al , 2018Yuan et al 2018;Zerr et al 2012b;Zhang et al 2005]. For example, Buschek et al [2015] presented an approach to assigning privacy to shared images using metadata (location, time, shot details) and visual features (faces, colors, edges).…”

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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Section: Related Workmentioning

confidence: 99%

Image Privacy Prediction Using Deep Neural Networks

Tonge

Caragea

2020

ACM Trans. Web

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Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction

Tonge

Caragea

2019

ACM Trans. Intell. Syst. Technol.

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Online images’ tags are very important for indexing, sharing, and searching of images, as well as surfacing images with private or sensitive content, which needs to be protected. Social media sites such as Flickr generate these metadata from user-contributed tags. However, as the tags are at the sole discretion of users, these tags tend to be noisy and incomplete. In this article, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images’ original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image, which are obtained from a large collection. Experimental results show that, although the user-input tags compose noise, our privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction and outperforms an existing privacy-oblivious approach to image tagging. The results also show that, even for images that do not have any user tags, our proposed approach can recommend accurate tags. Crowd-sourcing the predicted tags exhibits the quality of our privacy-aware recommended tags. Our code, features, and the dataset used in experiments are available at: https://github.com/ashwinitonge/privacy-aware-tag-rec.git.

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Obfuscation Remedies Harms Arising from Content Flagging of Photos

Caine

2022

CHI Conference on Human Factors in Computing Systems

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Towards privacy-aware photo sharing using mobile phones

Cited by 3 publications

References 5 publications

Image Privacy Prediction Using Deep Neural Networks

Image Privacy Prediction Using Deep Neural Networks

Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction

Obfuscation Remedies Harms Arising from Content Flagging of Photos

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