Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://github.com/ZNan-Chen/diffCOD.

本研究提出了一种基于扩散的伪装目标检测框架（diffCOD），该框架将伪装目标分割任务视为从噪声掩码到目标掩码的去噪扩散过程，并采用模型学习来逆转这一噪声处理过程。通过将输入图像先验编码和集成到去噪扩散模型中来加强去噪学习，同时使用注入注意力模块（IAM）通过交叉注意机制将从图像中提取的条件语义特征与扩散噪声嵌入相互作用以增强去噪学习。实验结果表明，在四个广泛使用的伪装目标检测基准数据集上，该方法相较于其他11种最先进的方法，尤其在伪装目标的纹理细分割上取得了有利的性能。

伪装目标检测的扩散模型