Cross-modal content security alignment method for multimodal generation

With the continuous progress of artificial intelligence technology, multimodal generation model has gradually become one of the core technologies in various applications. However, how to ensure the security and consistency of generated content, especially in cross-modal generation tasks, is still a difficult problem to be solved. In this paper, we propose a cross-modal content secure alignment approach for multimodal generation, which combines generative adversarial network (GAN) and multimodal embedding technology to ensure the secure alignment and consistency of generated content across all modes. By embedding modal data such as image, text and speech, and using discriminator in generative adversarial network to conduct security review of generated content, our proposed cross-modal content security alignment method can effectively reduce the content that does not meet the security standards. The experimental results show that in the cross-modal generation task based on COCO and Flickr30K dataset, the accuracy of the proposed method can reach 92.6%, while the generation rate of unsafe content is significantly lower than the existing method, and the reduction rate is up to 35%. In addition, the similarity score (CIDEr) of the proposed method in the task of image description generation and text generation is increased by 15.3% compared with the traditional method, showing relatively excellent generation quality. This research not only provides an effective cross-modal content security alignment method, but also provides a new idea and solution for the security of multi-modal generation systems, which has a wide application prospect and research value.