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4DPM: Deepfake Detection With a Denoising Diffusion Probabilistic Mask | IEEE Journals & Magazine | IEEE Xplore

4DPM: Deepfake Detection With a Denoising Diffusion Probabilistic Mask


Abstract:

In the face of increasingly realistic fake human faces, research on enhancing the differences between real and fake images is valuable for improving the generalization ca...Show More

Abstract:

In the face of increasingly realistic fake human faces, research on enhancing the differences between real and fake images is valuable for improving the generalization capabilities of fake face detection models. In this letter, we propose a method called DPMask (Diffusion Probabilistic Mask) to amplify the distinctions between authentic and counterfeit human facial images. Specifically, we use a dataset consisting of real human facial images and Simplex noise to train a denoising diffusion probabilistic model for the proposed DPMask. Subsequently, we separately apply the DPMask and U-Net to real and fake human facial images to create noticeably distinct genuine and counterfeit human facial images. A lightweight classification network blue is further designed based on RepVGG to classify the newly generated real and fake human faces. Experimental results demonstrate that our model achieves high accuracy on a manually created fake face dataset (RFFD), a GAN-generated fake face dataset (Seq-DeepFake), and a DDPM-generated face dataset (HiFi-IFDL). Furthermore, the addition of DPMask significantly improves the performance of some public fake face detection models.
Published in: IEEE Signal Processing Letters ( Volume: 31)
Page(s): 914 - 918
Date of Publication: 19 March 2024

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