Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

TL;DR

The results show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs, and paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

Abstract

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, firstly, we investigate how 13 most popular SNs treat the uploaded pictures, in order to identify a possible implementation of image watermarking techniques by respective SNs. Secondly, on these 13 SNs, we test the robustness of several image watermarking algorithms. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is robust enough in spite of the fact that the pictures get downgraded during the uploading process by SNs. The results of our analysis on a real dataset of 8,400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs.

Figures (6)

• Figure 1: User unaware watermarking's three main tasks: profile attribution (a), intra-layer user profiles linking (b), and inter-layer user profiles linking (c).
• Figure 2: The original image portion on the left, the portion of the SilentEye outcome in the middle, and the same SilentEye outcome with red circles highlighting artefacts on the right.
• Figure 3: Correlation results for each of the twelve fingerprints, six for the front cameras (first and second rows) and six for the rear cameras (third and fourth rows). $X$F and $X$R identify the front and the rear camera of the smartphone $X$, respectively.
• Figure 4: Profile attribution results. Each graph groups the results of a single source on all thirteen SNs, six for the front cameras (first and second rows) and six for the rear cameras (third and fourth rows). The number of images in each cell is identified through a white-to-blue scale, from 0 (white) to 17 (blue).
• Figure 5: Intra-layer user profiles linking results. Each graph groups the results of a single source on all thirteen SNs, six for the front cameras (first and second rows) and six for the rear cameras (second and third rows). The number of images in each cell is identified through a white-to-blue scale, from 0 (white) to 17 (blue).