Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.06528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918059795546112 |
|---|---|
| author | Hasan, Mahmudul Ruhama, Sadia Sithi, Sabrina Tajnim Samit, Chowdhury Mohammad Mutamir Saha, Oindrila |
| author_facet | Hasan, Mahmudul Ruhama, Sadia Sithi, Sabrina Tajnim Samit, Chowdhury Mohammad Mutamir Saha, Oindrila |
| contents | Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection Hasan, Mahmudul Ruhama, Sadia Sithi, Sabrina Tajnim Samit, Chowdhury Mohammad Mutamir Saha, Oindrila Computer Vision and Pattern Recognition Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset. |
| title | Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.06528 |