A New Wave of Texture Feature: Enhancing Deepfake Detection via Image Waveform

초록

To address the security threats posed by highly realistic deepfakes, various deepfake detection methods have been proposed. In particular, texture features resulting from the post-processing steps in deepfake generation, such as color correction and edge smoothing have been widely utilized in deepfake detection methods. However their detection method is limited against advanced image synthesis mechanisms. In this paper, we propose a novel deepfake detection method based on texture features, an Image Waveform. Our proposed method converts the pixel values of an image into an Image Waveform to enhance texture features and utilizes two-stream CNN to maximize deepfake detection performance by effectively leveraging the features of the original image. Experimental results of real-world deepfake dataset, confirmed that our proposed method achieves higher detection accuracy compared to existing texture feature-based detection methods. © 2024 IEEE.

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