Academic Journal of Computing & Information Science, 2025, 8(6); doi: 10.25236/AJCIS.2025.080607.
Deng Kaiwen, Long Yanbin
University of Science and Technology Liaoning, Anshan, China
With the rapid development of deep learning technology, the generation of deep forged images has become easier and easier, which brings many potential risks to the society. In order to effectively recognize deep forged images, this paper proposes a design of deep forged image recognition system that combines global and local texture features. The system extracts global and local texture features of the image, and uses deep learning techniques for feature fusion and classification to improve the accuracy and robustness of recognition. The experimental results show that the system achieves good recognition results on multiple datasets and has high practical application value.
Deepfake Image Detection; Global and Local Texture Features; Feature Fusion; Deep Learning; Image Classification
Deng Kaiwen, Long Yanbin. Design of a Deep Forgery Image Recognition System Combining Global and Local Texture Features. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 6: 56-63. https://doi.org/10.25236/AJCIS.2025.080607.
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