Images today are increasingly shared online on social networking sites such
as Facebook, Flickr, Foursquare, and Instagram. 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. 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. 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. Our
results show that even though the models trained on the visual features perform
better than those trained on the tag features, the combination of deep visual
features with image tags shows improvements in performance over the individual
feature sets.

本文研究了利用卷积神经网络和用户标签来自动预测图像的隐私，并将不同卷积神经网络中提取的深度特征进行比较，结果表明使用 ResNet 提取的特征的学习模型在图像隐私预测方面优于其他模型。同时，该研究结果还表明深度视觉特征和图像标签的组合可以提高预测性能。