ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection

Eun-Gi Lee, Isack Lee and Seok-Bong Yoo ()
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Eun-Gi Lee: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
Isack Lee: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
Seok-Bong Yoo: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea

Mathematics, 2023, vol. 11, issue 18, 1-17

Abstract: Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream convolutional neural network to capture deepfake clues unique to each feature. By exploiting these clues, we enhance the effectiveness and generalizability of deepfake detection. The experimental results demonstrate the improved performance and robustness of this method.

Keywords: deepfake detection; no-reference image quality assessment; discrete cosine transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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