Generalization challenges in video deepfake detection: methods, obstacles, and technological advances
With the rapid development of artificial intelligence, deepfake technology has become a powerful tool for generating realistic audio, images, and videos. However, its widespread use and decreasing costs pose serious threats to personal privacy and social trust. This paper reviews the generalization...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/109257432/ |
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| _version_ | 1849472507632943104 |
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| author | LI Junjie WANG Jianzong ZHANG Xulong QU Xiaoyang |
| author_facet | LI Junjie WANG Jianzong ZHANG Xulong QU Xiaoyang |
| author_sort | LI Junjie |
| collection | DOAJ |
| description | With the rapid development of artificial intelligence, deepfake technology has become a powerful tool for generating realistic audio, images, and videos. However, its widespread use and decreasing costs pose serious threats to personal privacy and social trust. This paper reviews the generalization issues in the field of video deepfake detection, providing a comprehensive overview of methods, challenges, and advancements. It first traces the development of deepfake detection techniques and compares the performance of neural networks with human capabilities in detection tasks. Regarding the challenges of cross-dataset detection, the paper analyzes how differences in feature distributions between datasets lead to the performance degradation of traditional deep learning models in cross-domain applications. To address this issue, researchers have proposed various solutions, such as neural network architecture design, multi-task learning, new loss functions, data augmentation, innovative training procedures, and the integration of biometric recognition technologies. In recent years, as deepfake detection research has reached a stage of stable development, it has also encountered bottlenecks, calling for breakthroughs. Through a systematic analysis of the latest research achievements in this field, this paper examines the strengths and limitations of current techniques, revealing both the potential and challenges in enhancing generalization and addressing unknown distribution domains. The insights provided offer valuable guidance for future research and applications. |
| format | Article |
| id | doaj-art-e9395e1bf3624786a6c920d8029ee917 |
| institution | Kabale University |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-e9395e1bf3624786a6c920d8029ee9172025-08-20T03:24:30ZzhoChina InfoCom Media Group大数据2096-02712025-01-01120109257432Generalization challenges in video deepfake detection: methods, obstacles, and technological advancesLI JunjieWANG JianzongZHANG XulongQU XiaoyangWith the rapid development of artificial intelligence, deepfake technology has become a powerful tool for generating realistic audio, images, and videos. However, its widespread use and decreasing costs pose serious threats to personal privacy and social trust. This paper reviews the generalization issues in the field of video deepfake detection, providing a comprehensive overview of methods, challenges, and advancements. It first traces the development of deepfake detection techniques and compares the performance of neural networks with human capabilities in detection tasks. Regarding the challenges of cross-dataset detection, the paper analyzes how differences in feature distributions between datasets lead to the performance degradation of traditional deep learning models in cross-domain applications. To address this issue, researchers have proposed various solutions, such as neural network architecture design, multi-task learning, new loss functions, data augmentation, innovative training procedures, and the integration of biometric recognition technologies. In recent years, as deepfake detection research has reached a stage of stable development, it has also encountered bottlenecks, calling for breakthroughs. Through a systematic analysis of the latest research achievements in this field, this paper examines the strengths and limitations of current techniques, revealing both the potential and challenges in enhancing generalization and addressing unknown distribution domains. The insights provided offer valuable guidance for future research and applications.http://www.j-bigdataresearch.com.cn/zh/article/109257432/video deepfake detectionface forgery detectiongeneralization capability |
| spellingShingle | LI Junjie WANG Jianzong ZHANG Xulong QU Xiaoyang Generalization challenges in video deepfake detection: methods, obstacles, and technological advances 大数据 video deepfake detection face forgery detection generalization capability |
| title | Generalization challenges in video deepfake detection: methods, obstacles, and technological advances |
| title_full | Generalization challenges in video deepfake detection: methods, obstacles, and technological advances |
| title_fullStr | Generalization challenges in video deepfake detection: methods, obstacles, and technological advances |
| title_full_unstemmed | Generalization challenges in video deepfake detection: methods, obstacles, and technological advances |
| title_short | Generalization challenges in video deepfake detection: methods, obstacles, and technological advances |
| title_sort | generalization challenges in video deepfake detection methods obstacles and technological advances |
| topic | video deepfake detection face forgery detection generalization capability |
| url | http://www.j-bigdataresearch.com.cn/zh/article/109257432/ |
| work_keys_str_mv | AT lijunjie generalizationchallengesinvideodeepfakedetectionmethodsobstaclesandtechnologicaladvances AT wangjianzong generalizationchallengesinvideodeepfakedetectionmethodsobstaclesandtechnologicaladvances AT zhangxulong generalizationchallengesinvideodeepfakedetectionmethodsobstaclesandtechnologicaladvances AT quxiaoyang generalizationchallengesinvideodeepfakedetectionmethodsobstaclesandtechnologicaladvances |