Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

• URL: http://arxiv.org/abs/2412.02575v1
• Date: Tue, 03 Dec 2024 17:02:40 GMT
• Title: Copy-Move Forgery Detection and Question Answering for Remote Sensing Image
• Authors: Ze Zhang, Enyuan Zhao, Ziyi Wan, Jie Nie, Xinyue Liang, Lei Huang,
• Abstract summary: This paper introduces the task of Remote Sensing Copy-Move Question Answering (RSCMQA)<n>Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios.<n>We have developed an accurate and comprehensive global dataset for remote sensing image copy-move question answering, named RS-CMQA-2.1M.
• Score: 14.436863648867904
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces the task of Remote Sensing Copy-Move Question Answering (RSCMQA). Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios and inferring relationships between objects. Based on the practical needs of national defense security and land resource monitoring, we have developed an accurate and comprehensive global dataset for remote sensing image copy-move question answering, named RS-CMQA-2.1M. These images were collected from 29 different regions across 14 countries. Additionally, we have refined a balanced dataset, RS-CMQA-B, to address the long-standing issue of long-tail data in the remote sensing field. Furthermore, we propose a region-discriminative guided multimodal CMQA model, which enhances the accuracy of answering questions about tampered images by leveraging prompt about the differences and connections between the source and tampered domains. Extensive experiments demonstrate that our method provides a stronger benchmark for RS-CMQA compared to general VQA and RSVQA models. Our dataset and code are available at https://github.com/shenyedepisa/RSCMQA.

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