Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition

Akhtar, Zahid; Dasgupta, Dipankar; Banerjee, Bonny

doi:10.2139/ssrn.3419272

Cited by 28 publications

(17 citation statements)

References 30 publications

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“…At the time of authentication, a probe sample is captured, processed in the same way, and the resulting probe template is compared against a reference template of a claimed identity (verification) or up to all stored reference templates (identification). 2 As a result, a (set of) biometric comparison score(s) is compared against a pre-defined threshold yielding acceptance or rejection decision. These processes are illustrated in Fig.…”

Section: Processesmentioning

confidence: 99%

Digital Face Manipulation in Biometric Systems

Ibsen

Rathgeb

Fischer

et al. 2022

Advances in Computer Vision and Pattern Recognition

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Biometric technologies, in particular facerecognition, are employed in many personal, commercial, and governmental identity management systems around the world. The processing of digitally manipulated face images within a face recognition system may lead to false decisions and thus decrease the reliability of the decision system. This necessitates the development of manipulation detection modules which can be seamlessly integrated into the processing chain of face recognition systems. This chapter discusses the impact of face image manipulation on face recognition technologies. To this end, the basic processes and key components of biometric systems are briefly introduced with particular emphasis on facial recognition. Additionally, face manipulation detection scenarios and concepts of how to integrate detection methods to face recognition systems are discussed. In an experimental evaluation, it is shown that different types of face manipulation, i.e. retouching, face morphing, and swapping, can significantly affect the biometric performance of face recognition systems and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face manipulation poses to face recognition technologies.

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Section: Processesmentioning

confidence: 99%

Digital Face Manipulation in Biometric Systems

Ibsen

Rathgeb

Fischer

et al. 2022

Advances in Computer Vision and Pattern Recognition

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show abstract

“…Overall, existing works have demonstrated, beyond doubt, that “morphing” is a threat for face recognition systems. Several survey papers have also highlighted the vulnerability of face recognition algorithm against digital manipulation and limitations of existing detection algorithms ( Akhtar et al, 2019 ; Scherhag et al, 2019 ; Singh et al, 2020 ; Tolosana et al, 2020 ; Venkatesh et al, 2020 ). The survey papers bring out the boundaries of existing detection algorithms such as non-generalizability against manipulation types and image resolution and computationally inefficiency.…”

Section: Related Workmentioning

confidence: 99%

MagNet: Detecting Digital Presentation Attacks on Face Recognition

Singh

Vatsa

Noore

2021

Front. Artif. Intell.

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Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.

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“…The first group needs to have original face images manipulated without losing important attributes like identity, while algorithms in the second group synthesize face images using semantic domains. [33] Manipulating face's attributes is more challenging than other image generation problems due to the fact that some image's features have to be modified while others need to remain unchanged. [34] Since the invention of GANs, many GAN-based methods have been designed for manipulating face images.…”

Section: Face Manipulationmentioning

confidence: 99%

Image Generation with Gans-based Techniques: A Survey

Esfahani¹,

Latifi²

2019

IJCSIT

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In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.

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Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition

Cited by 28 publications

References 30 publications

Digital Face Manipulation in Biometric Systems

Digital Face Manipulation in Biometric Systems

MagNet: Detecting Digital Presentation Attacks on Face Recognition

Image Generation with Gans-based Techniques: A Survey

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