Detection of Automated Facial Beautification by a Camera Application by Comparing a Face to a Rearranged Face

This publication describes systems and techniques to detect if a camera application is applying an automated mechanism to beautify a human face present in a photograph. Some camera applications may automatically adjust characteristics of a face to “beautify” them, such as by changing a skin tone. Although provided as a feature, this “beautification” potentially can be interpreted as cultural insensitivity or can result in mental health concerns. Accordingly, enabling a user to at least have knowledge of automated beautification can be beneficial. Detecting beautification is accomplished by determining if a camera application adjusts pixels corresponding to a face but not pixels corresponding to a rearranged face. Facial recognition software recognizes the face but fails to recognize the rearranged face. A tile from the face is compared to a corresponding tile from the rearranged face. If the two tiles are sufficiently dissimilar, then the system infers that the camera application has adjusted the pixels of the tile corresponding to the original facial image responsive to recognizing those pixels as part of a face. In this manner, the automated facial beautification can be detected

On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.Comment: Accepted in Senior Member Track, AAAI202

Impact and Detection of Facial Beautification in Face Recognition: An Overview

International audienceFacial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field