# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 2 Related Work
### Privacy Methods for Image Sharing in Social Networks  
[PERSON] et al. collected a dataset of images labeled according to their privacy level [12]. They designed a game where the participants were given a set of images and had to decide whether each image should be marked as 'private' or 'public'. Altogether, they collected more than 37,000 images publicly available from Flickr ([[https://www.flickr.com/](https://www.flickr.com/)]([https://www.flickr.com/](https://www.flickr.com/)), accessed on 20 May 2021) which had at least five tags each describing the image content. We used that dataset to experimentally evaluate our methods for automatically determining the privacy level of images shared on social networks. Different methods were devised to automatically determine the privacy level of an image. Usually, they use standard machine learning algorithms, and perform classification based on different types of features extracted from the image. We can broadly classify feature types into visual features or textual features. The former extract characteristics from the visual content of the image. Face detection features rely on computer vision tools to detect the occurrence of faces in a picture. For instance, using the Viola-Jones face detector [13], it is possible to extract two features: i.e., the number of faces detected in the image, and a vector of the bounding boxes around each detected face. The presence of people's faces could be a hint of the sensitivity of a picture [14]. [PERSON] et al. hypothesized that the distribution of colors may be an indicator of the degree of sensitivity of an image [12]. They computed the histogram of an image based on HS (i.e., Hue, Saturation) color space: each of the two color dimensions was split into sixteen evenly divided segments, resulting in a histogram consisting of 32 bins. They observed that public images tend to have fewer but more intense colors, whereas private images tend to have more colors which are evenly distributed across the histogram. Previous research showed that edge-based features could be used to discriminate outdoor vs. indoor images [15; 16]. It was found that private images tend to contain strong, straight and almost vertical edges, associated with indoor environments, whereas public images had shorter and weaker edges [15]. Since the location where a picture was taken may determine its privacy level, this is an important feature to consider. The presence of certain objects in an image may be useful to represent its context, which in turn can have an impact on the sensitivity of the picture. Object detection can be performed using different methods. The scale invariant feature transform (SIFT) descriptor uses a 128-dimensional vector to describe a texture found in an image. Each feature obtained through SIFT is then put inside a vocabulary of visual terms. Interesting image regions can be recognized through the detection of peaks in a difference of a Gaussian pyramid [17]. Different authors, including [PERSON] et al. [14] and [PERSON] et al. [18], have used these features for inferring the sensitivity level of images. To have a better insight into the presence of faces, in this work, we consider not only the number of recognized faces, but also the ratio of the image that they cover, in order to assess whether they are prevalent or not. Images shared on social networks are often tagged with user-defined keywords and comments. Previous works tried to extract textual content from tags in order to recognize the image privacy level, using methods normally used for text classification. It was found that, to some extent, there is correlation between the occurrence of certain tags and the sensitivity of the associated image [12]. [PERSON] and [PERSON] used deep visual semantic features derived from layers of convolutional neural networks, as well as textual features, to infer the privacy level of images [19]. Deep learning methods are also used by [PERSON] et al.