MFFI is a 1,024,000-image face forgery benchmark spanning 50 (listed 51) forgery techniques, varied facial scenes, multiple real-data sources, and multi-level degradation operations.
StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotation
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abstract
StyleGAN can use style to affect facial posture and identity features, and noise to affect hair, wrinkles, skin color and other details. Among these, the outcomes of the picture processing will vary slightly between different versions of styleGAN. As a result, the comparison of performance differences between styleGAN2 and the two modified versions of styleGAN3 will be the main focus of this study. We used the FFHQ dataset as the dataset and FID, EQ-T, and EQ-R were used to be the assessment of the model. In the end, we discovered that Stylegan3 version is a better generative network to improve the equivariance. Our findings have a positive impact on the creation of animation and videos.
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MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
MFFI is a 1,024,000-image face forgery benchmark spanning 50 (listed 51) forgery techniques, varied facial scenes, multiple real-data sources, and multi-level degradation operations.