An Introduction to Digital Face Manipulation

Abstract

Digital manipulation has become a thriving topic in the last few years, especially after the popularity of the term DeepFakes. This chapter introduces the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: (i) entire face synthesis, (ii) identity swap, (iii) face morphing, (iv) attribute manipulation, (v) expression swap (a.k.a. face reenactment or talking faces), and (vi) audio- and text-to-video. These six main types of face manipulation are well established by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.

IRL-Net: Inpainted Region Localization Network via Spatial Attention

“…Even recent advances in inpainting methods show that they can fill large missing areas with meaningful structures and objects that do not exist anywhere else in the image [9]. Such advancements make the manipulation detection a very challenging process [13], especially when the aim is not only to discriminate manipulated images from the authentic ones, but also to pinpoint tampered regions at the pixel level [14]. Notably, different categories of GAN-based inpainting methods [15] are trained using various sizes of masks which enable them to predict small or large masked regions, leading to, as shown in Fig.…”