Copy-Move Forgery Detection and Question Answering for Remote Sensing Image
This addresses the need for improved security and monitoring in land resource and defense applications by advancing forgery detection in remote sensing, though it appears incremental as it builds on existing RSVQA with a new focus on tampering scenarios.
This paper tackles the problem of detecting and interpreting copy-move forgeries in remote sensing images by introducing the Remote Sensing Copy-Move Question Answering (RSCMQA) task and creating five comprehensive datasets from 29 regions across 14 countries, with their proposed CMFPF framework providing a stronger benchmark compared to existing VQA and RSVQA models.
Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question Answering (RSCMQA) task. Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios and inferring relationships between objects. We present a suite of global RSCMQA datasets, comprising images from 29 different regions across 14 countries. Specifically, we propose five distinct datasets, including the basic dataset RS-CMQA, the category-balanced dataset RS-CMQA-B, the high-authenticity dataset Real-RSCM, the extended dataset RS-TQA, and the extended category-balanced dataset RS-TQA-B. These datasets fill a critical gap in the field while ensuring comprehensiveness, balance, and challenge. Furthermore, we introduce a region-discrimination-guided multimodal copy-move forgery perception framework (CMFPF), 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 RSCMQA compared to general VQA and RSVQA models. Our datasets and code are publicly available at https://github.com/shenyedepisa/RSCMQA.