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An Overview of Fake Face Detection Approaches

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Progress in Intelligent Decision Science (IDS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1301))

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Abstract

Human face has been one of the most popular objects in computer vision. With the advancements in computing power over the past decades, deep face understanding systems have reached (and surpassed at some cases) human performance and hence, the problem of facial analysis has changed its route from analyzing to generating. These adversarial generation techniques have been used in generating fake news, fake political and financial statements, and even fake porn videos, leveraging the wide availability of large-scale public databases and machine learning techniques. However, there are only a few studies to detect the generated face from real. This study investigates the abundantly used fake face generation models and examines the techniques for detecting facial manipulation.

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Authors and Affiliations

  1. Bahcesehir University, Istanbul, Turkey

    Duygu Cakir, Merve Aritürk & Özge Yücel

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  1. Duygu Cakir
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  2. Merve Aritürk
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  3. Özge Yücel
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Correspondence to Duygu Cakir .

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Editors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

    Tofigh Allahviranloo

  2. Faculty of Engineering and Natural Sciences, Bahçeşehir University, Istanbul, Turkey

    Soheil Salahshour

  3. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

    Nafiz Arica

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Cakir, D., Aritürk, M., Yücel, Ö. (2021). An Overview of Fake Face Detection Approaches. In: Allahviranloo, T., Salahshour, S., Arica, N. (eds) Progress in Intelligent Decision Science. IDS 2020. Advances in Intelligent Systems and Computing, vol 1301. Springer, Cham. https://doi.org/10.1007/978-3-030-66501-2_16

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