Two-stream convolutional neural network for image source social network identification

The identification of the source social network from an image is a relatively new research area in the image forensic domain. The classification of the source social network can be a crucial element for the growing number of cases of social-media related crimes, such as cyberbullying. This paper takes into consideration the state-of-the-art approaches

addressing this problem and proposes a new methodology to improve the results obtained to date. Our identification technique is based on the idea that social networks perform

some processing on the uploaded images, such as resizing or re-compression, and leave some artifacts on them. We propose to use DCT features and image noise residual analysis to detect such artifacts. A two-stream convolutional neural network, which combines the inputs from these two artifact domains, is trained to classify the source social network of images coming from three different datasets. This paper explores the two domains, proposes strategies for managing unbalanced datasets, provides details about the proposed two-stream CNN, and presents the results achieved by our method compared with

the current state-of-the-art approaches.