| Title |
Research on Detection of Digital Video Forgery from a Legal Perspective |
| Authors |
(Feng Wang) ; (Yong Zhong Cuo Mu) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.284 |
| Keywords |
Accuracy; Convolutional neural network; Digital video; Forgery; Legal |
| Abstract |
With the increasing proliferation of digital video forgery, detecting such forgery has become increasingly important due to the inadequacy of existing laws. This paper briefly analyzed the harm caused by digital video forgery and the current legal regulations. Then, EfficientNet as a method for detecting digital video forgery was introduced. EfficientNet-V2 was optimized through the integration of the convolutional block attention module and the Mish function. Experiments were performed on the improved EfficientNet-V2 using existing datasets of forged digital videos. A significant improvement was observed in the accuracy of the improved EfficientNet-V2.
The accuracy for LQ and HQ in FF++ were 83.45% and 95.21%, respectively, while the accuracy for DFDC and Celeb-DF were 97.66% and 99.17%, respectively. These results outperformed existing detection methods such as MesoNet. The improved EfficientNet-V2 also showed good generalization ability in cross-domain experiments. The findings validate the effectiveness of the proposed method for detecting forged digital videos, making it suitable for practical application and promotion. |