FragFake: A Dataset for Fine-Grained Detection of Edited Images with Vision Language Models

• URL: http://arxiv.org/abs/2505.15644v1
• Date: Wed, 21 May 2025 15:22:45 GMT
• Title: FragFake: A Dataset for Fine-Grained Detection of Edited Images with Vision Language Models
• Authors: Zhen Sun, Ziyi Zhang, Zeren Luo, Zeyang Sha, Tianshuo Cong, Zheng Li, Shiwen Cui, Weiqiang Wang, Jiaheng Wei, Xinlei He, Qi Li, Qian Wang,
• Abstract summary: We develop FragFake, the first dedicated benchmark dataset for edited image detection.<n>We use Vision Language Models (VLMs) for the first time in the task of edited image classification and edited region localization.<n>This work is the first to reformulate localized image edit detection as a vision-language understanding task.
• Score: 48.85744313139525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-grained edited image detection of localized edits in images is crucial for assessing content authenticity, especially given that modern diffusion models and image editing methods can produce highly realistic manipulations. However, this domain faces three challenges: (1) Binary classifiers yield only a global real-or-fake label without providing localization; (2) Traditional computer vision methods often rely on costly pixel-level annotations; and (3) No large-scale, high-quality dataset exists for modern image-editing detection techniques. To address these gaps, we develop an automated data-generation pipeline to create FragFake, the first dedicated benchmark dataset for edited image detection, which includes high-quality images from diverse editing models and a wide variety of edited objects. Based on FragFake, we utilize Vision Language Models (VLMs) for the first time in the task of edited image classification and edited region localization. Experimental results show that fine-tuned VLMs achieve higher average Object Precision across all datasets, significantly outperforming pretrained models. We further conduct ablation and transferability analyses to evaluate the detectors across various configurations and editing scenarios. To the best of our knowledge, this work is the first to reformulate localized image edit detection as a vision-language understanding task, establishing a new paradigm for the field. We anticipate that this work will establish a solid foundation to facilitate and inspire subsequent research endeavors in the domain of multimodal content authenticity.

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