Face Morphing Detection in Social Media Content

Abstract

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Face being an active medium of communication is a significant part of our social media life; however, faces are vulnerable to manipulations. Among various manipulations, face morphing is a well-known tampering technique that aims to generate images containing information from more than one identity. Morphed images are heavily used for various malicious purposes including sarcasm, money laundering, and pornography. For many of the above harmful purposes, these manipulated images are uploaded on social media platforms where they can further go through tampering using social-media filters. Interestingly, the existing morph attack detection works have not addressed social media’s impact on deceiving face morph detectors. In this research, for the first time, we have generated authentic (or real) and face-morphed images impacted by one of the premium features of social media platforms known as filtering. We have used 13 Instagram filters and performed an extensive study on the proposed social-media morphed dataset. It is demonstrated that these filters can radically reduce the morph detection performances of several popular deep-learning classifiers. Therefore, to effectively address the concerns of face morphing and social media filtering, we propose a robust ViT-CNN architecture to advance the morph image detection performance.