Trinity Detector:text-assisted and attention mechanisms based spectral fusion for diffusion generation image detection
<|reference_start|>Trinity Detector:text-assisted and attention mechanisms based spectral fusion for diffusion generation image detection: 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.<|reference_end|>
@article{song2024trinity,
    title={Trinity Detector:text-assisted and attention mechanisms based spectral
  fusion for diffusion generation image detection},
    author={Jiawei Song, Dengpan Ye, Yunming Zhang},
    journal={arXiv preprint arXiv:2404.17254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17254},
    primaryClass={cs.CV}
}