Lightweight Deepfake Detection Based on Multi-Feature Fusion
Deepfake technology utilizes deep learning (DL)-based face manipulation techniques to seamlessly replace faces in videos, creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment, misuse of its capabilities may lead...
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/1954 |
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| author | Siddiqui Muhammad Yasir Hyun Kim |
| author_facet | Siddiqui Muhammad Yasir Hyun Kim |
| author_sort | Siddiqui Muhammad Yasir |
| collection | DOAJ |
| description | Deepfake technology utilizes deep learning (DL)-based face manipulation techniques to seamlessly replace faces in videos, creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment, misuse of its capabilities may lead to serious risks, including identity theft, cyberbullying, and false information. The integration of DL with visual cognition has resulted in important technological improvements, particularly in addressing privacy risks caused by artificially generated “deepfake” images on digital media platforms. In this study, we propose an efficient and lightweight method for detecting deepfake images and videos, making it suitable for devices with limited computational resources. In order to reduce the computational burden usually associated with DL models, our method integrates machine learning classifiers in combination with keyframing approaches and texture analysis. Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively. |
| format | Article |
| id | doaj-art-dc82b85604fe40c59e60114278fb6a3e |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-dc82b85604fe40c59e60114278fb6a3e2025-08-20T02:44:55ZengMDPI AGApplied Sciences2076-34172025-02-01154195410.3390/app15041954Lightweight Deepfake Detection Based on Multi-Feature FusionSiddiqui Muhammad Yasir0Hyun Kim1Department of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaDepartment of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaDeepfake technology utilizes deep learning (DL)-based face manipulation techniques to seamlessly replace faces in videos, creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment, misuse of its capabilities may lead to serious risks, including identity theft, cyberbullying, and false information. The integration of DL with visual cognition has resulted in important technological improvements, particularly in addressing privacy risks caused by artificially generated “deepfake” images on digital media platforms. In this study, we propose an efficient and lightweight method for detecting deepfake images and videos, making it suitable for devices with limited computational resources. In order to reduce the computational burden usually associated with DL models, our method integrates machine learning classifiers in combination with keyframing approaches and texture analysis. Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively.https://www.mdpi.com/2076-3417/15/4/1954deepfake detectionfeature fusionHistogram of Oriented Gradients (HOG)Local Binary Pattern (LBP)KAZE descriptors |
| spellingShingle | Siddiqui Muhammad Yasir Hyun Kim Lightweight Deepfake Detection Based on Multi-Feature Fusion Applied Sciences deepfake detection feature fusion Histogram of Oriented Gradients (HOG) Local Binary Pattern (LBP) KAZE descriptors |
| title | Lightweight Deepfake Detection Based on Multi-Feature Fusion |
| title_full | Lightweight Deepfake Detection Based on Multi-Feature Fusion |
| title_fullStr | Lightweight Deepfake Detection Based on Multi-Feature Fusion |
| title_full_unstemmed | Lightweight Deepfake Detection Based on Multi-Feature Fusion |
| title_short | Lightweight Deepfake Detection Based on Multi-Feature Fusion |
| title_sort | lightweight deepfake detection based on multi feature fusion |
| topic | deepfake detection feature fusion Histogram of Oriented Gradients (HOG) Local Binary Pattern (LBP) KAZE descriptors |
| url | https://www.mdpi.com/2076-3417/15/4/1954 |
| work_keys_str_mv | AT siddiquimuhammadyasir lightweightdeepfakedetectionbasedonmultifeaturefusion AT hyunkim lightweightdeepfakedetectionbasedonmultifeaturefusion |