Latent Diffusion Models for Camouflage Generation: A Pipeline for Contextual Pattern Generation and Assessment

Sammanfattning

Traditional camouflage development relies heavily on manual design and iterative field testing, which are resource-intensive and limited in adaptability. This thesis explores the use of generative artificial intelligence for the design of camouflage patterns, with a focus on Latent Diffusion Models (LDMs), such as Stable Diffusion 3 (SD3). A method is proposed for generating context-specific patterns that visually blend into natural envi- ronments, using a pipeline that transforms target backgrounds into shuffled data grids as input to the generative model, with the possibility of applying structural segmentation and a constrained color palette. To evaluate the effectiveness of the generated camouflage, both computational metrics such as Image Color Similarity Index(ICSI) and Gradient Magnitude Standard Devia- tion(GMSD) is used in conjunction with a human visual assessment. Results show that key preprocessing parameters, particularly image block size and latent strength, have a significant impact on the quality. While digital patterns showed promising alignment be- tween human perception and edge- and color-based computational metrics, SSIM proved unreliable for camouflage assessment. Printed patterns presented additional challenges due to material properties influencing visibility. The findings suggest that LDMs can produce effective camouflage patterns suitable for specific environments, although improvements in environment representation and evaluation are possible.