Generation of dynamic images for fake-face detection

Enkhtaivan Purevsuren; Uuganbayar Ganbold; Akashi Takuya

doi:10.1117/12.2625235

30 April 2022 Generation of dynamic images for fake-face detection

Enkhtaivan Purevsuren, Uuganbayar Ganbold, Akashi Takuya

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121773L (2022) https://doi.org/10.1117/12.2625235
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Recently, a video of famous politicians and superstars giving certain speeches surfaced online causing severe political and commercial problems. The video, although it seemed authentic, was fake. Therefore, in this study, using 37,936 videos sampled from the deep fake detection challenge (DFDC), we developed an efficient and highly accurate deepfake detection system using EfficientNet with dynamic images. Dynamic image transforms video sequences into one frame instance by conserving spatiotemporal information. The experimental results and comparative analysis indicate that EfficientNet with dynamic image exhibits higher performance than EfficientNet. We also found that dynamic images generated by 20 frames have a higher fake-face detection accuracy than simple images.

Citation Download Citation

Enkhtaivan Purevsuren, Uuganbayar Ganbold, and Akashi Takuya "Generation of dynamic images for fake-face detection", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121773L (30 April 2022); https://doi.org/10.1117/12.2625235

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KEYWORDS

Video

Video acceleration

Facial recognition systems

RGB color model

Binary data

Imaging systems

Performance modeling

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