Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910290479677440 |
|---|---|
| author | Guarnera, Luca Manganello, Salvatore Battiato, Sebastiano |
| author_facet | Guarnera, Luca Manganello, Salvatore Battiato, Sebastiano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11204 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis Guarnera, Luca Manganello, Salvatore Battiato, Sebastiano Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2310.11204 |