Image processing models have been widely applied across various scenarios, making the protection of their intellectual property increasingly important to prevent unauthorized use. However, existing watermarking methods are facing various problems, such as high-frequency artifacts, reduced model efficiency, and insufficient imperceptibility. To address these problems, this study proposes a black-box watermarking method with adaptive camouflage for image processing models. The method generates naturally blended camouflage textures as trigger patterns by extracting image color features and designs a recognition and transformation module to convert trigger images into high-quality watermarked images. It dynamically extracts dominant color features using the HLS histogram filtering and a local clustering algorithm, and enhances texture imperceptibility through Gaussian filtering and feathered masking techniques, ensuring that the watermark introduces no visual artifacts in either the spatial or frequency domains. Experimental results demonstrate that the proposed method preserves model fidelity, achieves a 100% watermark verification rate, and maintains robustness against various watermark removal and attack strategies such as fine-tuning and pruning.