You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online every day, the development of effective and efficient prediction methods for image privacy settings are highly needed. The performance of models critically depends on the choice of the feature representation. In this paper, we present an approach to image privacy prediction that uses deep features and deep image tags as feature representations. Specifically, we explore deep features at various neural network layers and use the top layer (probability) as an auto-annotation mechanism. The results of our experiments show that models trained on the proposed deep features and deep image tags substantially outperform baselines such as those based on SIFT and GIST as well as those that use "bag of tags" as features.

Cited Abstract(s):
[1]: Sharing personal information and documents is pervasive in Web 2.0 environments, which creates the need for properly controlling shared data. Most existing authorization and policy management systems are for organizational use by IT professionals. Average Web users, however, do not have the sophistication to specify and maintain privacy policies for their shared content. In this paper, we aim to utilize personal and social annotations to develop automatic tools for managing content sharing, and demonstrate a new application of social annotations in access control. We use annotation data to predict privacy preferences of users and automatically derive policies for shared content. We carry out a series of user studies to evaluate the accuracy of our predicted techniques. We also perform extensive analysis on static and dynamic approaches of analyzing semantic similarities of tags, which is of independent interest. Our analysis gives encouraging results on the feasibility of using annotations for privacy management in Web 2.0.

[2]: While photo sharing sites such as Flickr provide efficient tools for setting up an online album, users who want to maintain a certain level of privacy are usually only provided with rudimentary access control. Given that descriptive tags are extensively used on photos, and that the Semantic Web provides a common means of sharing social network information as linked data, we believe better access control mechanism can be provided by combining the two. Based on this idea, we propose and describe in this paper a system which allows users to create expressive access control policies for their photos on the Web by using both tags and linked data.

[3]: This paper reviews the state of the art and some emerging issues in research areas related to pattern analysis and monitoring of web-based social communities. This research area is important for several reasons. First, the presence of near-ubiquitous low-cost computing and communication technologies has enabled people to access and share information at an unprecedented scale. The scale of the data necessitates new research for making sense of such content. Furthermore, popular websites with sophisticated media sharing and notification features allow users to stay in touch with friends and loved ones; these sites also help to form explicit and implicit social groups. These social groups are an important source of information to organize and to manage multimedia data. In this article, we study how media-rich social networks provide additional insight into familiar multimedia research problems, including tagging and video ranking. In particular, we advance the idea that the contextual and social aspects of media are as important for successful multimedia applications as is the media content. We examine the inter-relationship between content and social context through the prism of three key questions. First, how do we extract the context in which social interactions occur? Second, does social interaction provide value to the media object? Finally, how do social media facilitate the repurposing of shared content and engender cultural memes? We present three case studies to examine these questions in detail. In the first case study, we show how to discover structure latent in the social media data, and use the discovered structure to organize Flickr photo streams. In the second case study, we discuss how to determine the interestingness of conversations—and of participants—around videos uploaded to YouTube. Finally, we show how the analysis of visual content, in particular tracing of content remixes, can help us understand the relationship among YouTube participants. For each case, we present an overview of recent work and review the state of the art. We also discuss two emerging issues related to the analysis of social networks—robust data sampling and scalable data analysis.

[4]: Users often have rich and complex photo-sharing preferences, but properly configuring access control can be difficult and time-consuming. In an 18-participant laboratory study, we explore whether the keywords and captions with which users tag their photos can be used to help users more intuitively create and maintain access-control policies. We find that (a) tags created for organizational purposes can be repurposed to create efficient and reasonably accurate access-control rules; (b) users tagging with access control in mind develop coherent strategies that lead to significantly more accurate rules than those associated with organizational tags alone; and (c) participants can understand and actively engage with the concept of tag-based access control.
Several works were conducted in the context of tag-based access control policies for images [2] [4] [1] , showing some initial success in tying tags with access control rules. However, the scarcity of tags for many online images [3] , and the workload associated with user-defined tags precluded accurate analysis of images' sensitivity based on this dimension.