Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering

In this work, we introduce Camouflage Anything, a novel and robust approach to generate camouflaged datasets. To the best of our knowledge, we are the first to apply Controlled Out-painting and Representation Engineering for generating realistic camouflaged images with an objective to hide any segmented object coming from a generic or salient database. Our proposed method uses a novel control design to out-paint a given segmented object, with a camouflaged background. We also uncover the role of representation engineering in enhancing the quality of generated camouflage datasets. We address the limitations of existing metrics FID and KID in capturing the "camouflage quality", by proposing a novel metric namely, CamOT. CamOT uses Optimal Transport between foreground & background Gaussian Mixture Models (GMM) of concerned camouflaged object to assign an image quality score. Furthermore, we conduct LoRA-based fine-tuning of the robust BiRefNet baseline with our generated camouflaged datasets, leading to notable improvements in camouflaged object segmentation accuracy. The experimental results showcase the efficacy and potential of Camouflage Anything, outperforming existing methods in camouflaged generation tasks.