TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models

• URL: http://arxiv.org/abs/2504.14933v1
• Date: Mon, 21 Apr 2025 07:53:58 GMT
• Title: TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models
• Authors: Mazharul Islam Rakib, Showrin Rahman, Joyanta Jyoti Mondal, Xi Xiao, David Lewis, Alessandra Mileo, Meem Arafat Manab,
• Abstract summary: Copyright infringement is a major roadblock to the free sharing of images.<n>Some AI models have been noted to directly copy copyrighted images.<n>We propose a novel two-step image generation model inspired by the conditional diffusion model.
• Score: 42.845519045205144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In today's age of social media and marketing, copyright issues can be a major roadblock to the free sharing of images. Generative AI models have made it possible to create high-quality images, but concerns about copyright infringement are a hindrance to their abundant use. As these models use data from training images to generate new ones, it is often a daunting task to ensure they do not violate intellectual property rights. Some AI models have even been noted to directly copy copyrighted images, a problem often referred to as source copying. Traditional copyright protection measures such as watermarks and metadata have also proven to be futile in this regard. To address this issue, we propose a novel two-step image generation model inspired by the conditional diffusion model. The first step involves creating an image segmentation mask for some prompt-based generated images. This mask embodies the shape of the image. Thereafter, the diffusion model is asked to generate the image anew while avoiding the shape in question. This approach shows a decrease in structural similarity from the training image, i.e. we are able to avoid the source copying problem using this approach without expensive retraining of the model or user-centered prompt generation techniques. This makes our approach the most computationally inexpensive approach to avoiding both copyright infringement and source copying for diffusion model-based image generation.

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