Research on Deep Forgery Defense Technology Based on Artificial Intelligence Security

Deep forgery usually refers to the technique of multimedia forgery using deep learning. This technique if applied in illegal ways will threaten the security of individuals and society, in order to ensure the sustainable development of artificial intelligence technology and reduce its negative impact aspects, this study proposes an innovative detection model that aims to improve the accuracy of deep forgery content detection by combining the advantages of convolutional networks and capsule networks-BIGRU. The research methodology consists of first extracting spatial features of the image using a convolutional neural network (CNN), followed by capturing the positional information of the object using a capsule network, and capturing temporal features between video frames using a gated recurrent unit (GRU). The combined application of these techniques is extensively tested on multiple sample datasets. The experimental results show that the present model outperforms existing mainstream approaches in detecting deeply forged content, which not only proves the effectiveness of the model, but also demonstrates its potential in improving the security performance of AI. Ultimately, this study concludes that the accuracy of deep forgery detection can be significantly improved by comprehensively utilizing CNNs, capsule networks, and GRUs, which provides strong technical support for the healthy development of AI technology.