Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis
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Abstract (arXiv)
A new algorithm for the detection of deepfakes in digital videos is presented. The I-frames were extracted in order to provide faster computation and analysis than approaches described in the literature. To identify the discriminating regions within individual video frames, the entire frame, background, face, eyes, nose, mouth, and face frame were analyzed separately. From the Discrete Cosine Transform (DCT), the Beta components were extracted from the AC coefficients and used as input to standard classifiers. Experimental results show that the eye and mouth regions are those most discriminative and able to determine the nature of the video under analysis.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 17 Oct 2023.
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17 of 26 references.
References · 17
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5 more
- 2020Fighting Deepfake by Exposing the Convolutional Traces on Imagescited by 24
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