Scholarlink

<p>Partially manipulated images pose a growing threat to the reliability of online content. The rapid spread of diffusion-based inpainting tools has made the creation of such manipulations increasingly easy to perform. As a result, the multimedia forensics community is disadvantaged compared to the attackers, as developing effective localization techniques often requires the creation of large datasets, a resource-intensive process due to the necessary human effort. In this paper, we present Beyond the Brush+<b>+</b> (BtB++), a fully automated pipeline for generating large-scale datasets of realistic inpainted images. Our experiments demonstrate that BtB++ is both flexible and easily integrates different models and configurations, offering the adaptability required to address evolving models and application scenarios. Moreover, an automatic filtering mechanism ensures quality control by discarding low-quality generated images. To provide an initial assessment of the proposed filtering strategy, we also conducted a small-scale human evaluation, studying the alignment between human perceptual judgments and the automatic metrics used for filtering.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond the Brush++: a flexible pipeline for fully automated generation of realistic inpainted images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月23日

查看全文
  • Giulia Bertazzini,
  • Chiara Albisani,
  • Daniele Baracchi,
  • Dasara Shullani,
  • Alessandro Piva

摘要

Partially manipulated images pose a growing threat to the reliability of online content. The rapid spread of diffusion-based inpainting tools has made the creation of such manipulations increasingly easy to perform. As a result, the multimedia forensics community is disadvantaged compared to the attackers, as developing effective localization techniques often requires the creation of large datasets, a resource-intensive process due to the necessary human effort. In this paper, we present Beyond the Brush++ (BtB++), a fully automated pipeline for generating large-scale datasets of realistic inpainted images. Our experiments demonstrate that BtB++ is both flexible and easily integrates different models and configurations, offering the adaptability required to address evolving models and application scenarios. Moreover, an automatic filtering mechanism ensures quality control by discarding low-quality generated images. To provide an initial assessment of the proposed filtering strategy, we also conducted a small-scale human evaluation, studying the alignment between human perceptual judgments and the automatic metrics used for filtering.