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

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, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 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. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness

“…A photo response non-uniformitybased forgery detection method in image watermarking also proves their effectiveness in detecting forgeries of digital images [40]. User-unaware watermarks can be effectively used in detecting fake profiles of any social network, where the photo response non-uniformity method shows more effectiveness compared to the state-of-the-art methods [41]. In the future, we will work with generative adversarial networks, photo response non-uniformity, or user-unaware watermarks to enhance the robustness of our method.…”

A single watermark based scheme for both protection and authentication of identities

“…Note, the default image dimensions/size accepted by 13 prominent SNs, recently studied by Bertini et al. in [10], are given in Table 2. This table shows that all of these SNs are defaulted to accept an image with dimensions in the power of two.…”

“…Unfortunately, these platforms are also the primary source of information leaks, and according to Bertini et al. [10], only one out of 13 main SNs uses watermarking technology Before uploading an image of their work on a SN, artists can embed the electronic version of their art with a robust watermark. This means, if an artist comes across a stolen version of their work, they can alert the relevant SN, and prove the copyright of their work via the robust watermark.…”

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

“…Numerous watermarking schemes for the protection of concealed information, content authentication, and IPR protection have been proposed in numerous studies, either in the spatial or transform domain [3]- [5]. This section presents a summary of such previously proposed watermarking strategies.…”

“…In that regard, among the numerous methods adopted to protect CH data, digital watermarking has attracted considerable attention in the current scenario owing to its potential in certifying data authentication and validating ownership rights. Using this method, unrevealed data (called watermarks) are hidden inside digital data without threatening the confidentiality of the original data [3]. Digital watermarking methods can be typically categorized into two types: robust and fragile.…”