Abstract
Discriminating between computer-generated images and natural ones is a crucial problem in digital image forensics. Facial images belong to a special case of this problem. Advances in technology have made it possible for computers to generate realistic multimedia contents that are very difficult to distinguish from non-computer generated contents. This could lead to undesired applications such as face spoofing to bypass authentication systems and distributing harmful unreal images or videos on social media. We have created a method for identifying computer-generated facial images that works effectively for both frontal and angled images. It can also be applied to extracted video frames. This method is based on smoothness property of the faces presented by edges and human skin’s characteristic via local entropy. Experiments demonstrated that performance of the proposed method is better than that of state-of-the-art approaches.
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Acknowledgments
We would like to thank Dr. Duc-Tien Dang-Nguyen in the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy for providing the two datasets used for evaluation.
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Nguyen, H.H., Nguyen-Son, HQ., Nguyen, T.D., Echizen, I. (2016). Discriminating Between Computer-Generated Facial Images and Natural Ones Using Smoothness Property and Local Entropy. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_4
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DOI: https://doi.org/10.1007/978-3-319-31960-5_4
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