  Artificial Intelligence Generated Content (AIGC) techniques, represented by
text-to-image generation, have led to a malicious use of deep forgeries,
raising concerns about the trustworthiness of multimedia content. Adapting
traditional forgery detection methods to diffusion models proves challenging.
Thus, this paper proposes a forgery detection method explicitly designed for
diffusion models called Trinity Detector. Trinity Detector incorporates
coarse-grained text features through a CLIP encoder, coherently integrating
them with fine-grained artifacts in the pixel domain for comprehensive
multimodal detection. To heighten sensitivity to diffusion-generated image
features, a Multi-spectral Channel Attention Fusion Unit (MCAF) is designed,
extracting spectral inconsistencies through adaptive fusion of diverse
frequency bands and further integrating spatial co-occurrence of the two
modalities. Extensive experimentation validates that our Trinity Detector
method outperforms several state-of-the-art methods, our performance is
competitive across all datasets and up to 17.6\% improvement in transferability
in the diffusion datasets.
