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.

本文提出了一种针对扩散模型特别设计的伪造检测方法 —— 三位一体检测器，它通过使用 CLIP 编码器将粗粒度文本特征与像素域中的细粒度伪造物相结合，实现了综合多模态检测。通过设计多光谱通道注意力融合单元（MCAF），提取频谱不一致性，并进一步集成两种模态的空间共现性，以增加对扩散生成图像特征的敏感性。大量实验证明，我们的三位一体检测器方法优于多种最先进的方法，在所有数据集上都具有较强的竞争力，并在扩散数据集的可迁移性上提升了 17.6%。