This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. 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. Experimental evaluations conducted using the MIFS database and a subset of the DFW database reveal that deep face representations achieve competitive detection equal error rates of 0.7% and 1.8%, respectively. The classifiers are trained with a large number of synthetically generated makeup presentation attacks utilising a generative adversarial network for facial makeup transfer in conjunction with image warping. bona fide presentations, machine learning-based classifiers are used. To distinguish makeup presentation attacks from genuine, i.e. The proposed detection systems employ various types of feature extractors including texture descriptors, facial landmarks, and deep (face) representations. differential, attack detection schemes which analyse differences in feature representations obtained from potential makeup presentation attacks and corresponding target face images. Further, we propose different image pair-based, i.e. It is shown that makeup presentation attacks might seriously impact the security of face recognition systems. Specifically, we focus on makeup presentation attacks with the aim of impersonation employing the publicly available Makeup Induced Face Spoofing (MIFS) and Disguised Faces in the Wild (DFW) databases. Subsequently, we assess the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks. We provide a comprehensive survey of works related to the topic of makeup presentation attack detection, along with a critical discussion. More precisely, an attacker might apply heavy makeup to obtain the facial appearance of a target subject with the aim of impersonation or to conceal their own identity. Additionally, it was recently demonstrated that makeup can be abused to launch so-called makeup presentation attacks. The application of facial cosmetics may cause substantial alterations in the facial appearance, which can degrade the performance of facial biometrics systems. The experimental evaluation conducted over several databases shows a high generalisation capability of the proposed method for detecting unknown attacks in both the digital and physical domains. To tackle this problem, we introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images (i.e., reference and probe) and then combined for identity attack detection. In this context, most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time. Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation. Despite recent advances, face recognition systems have shown to be particularly vulnerable to identity attacks (i.e., digital manipulations and attack presentations). Due to their convenience and high accuracy, face recognition systems are widely employed in governmental and personal security applications to automatically recognise individuals.
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