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<p>Remote sensing imagery plays a crucial role in diverse applications such as environmental monitoring, urban planning, and disaster response. However, the increasing availability of such data has also raised concerns about remote sensing image integrity. This is a survey article related to forgery detection in remote sensing images, with a focus on copy-move manipulations, which are particularly challenging to identify due to their local nature and high visual similarity to surrounding regions. Our experimentation regarding spectral domain analysis using hyperspectral imagery revealed some detectable anomalies in band-wise reflectance patterns in forged images. The impact of forgery is tested on Ultralytics YOLOv11-based object detection model. The findings underscore the necessity of incorporating robust forgery detection mechanisms before deploying remote sensing data for sensitive applications. Without such measures, forged images may go unnoticed, potentially leading to erroneous interpretations and decisions.</p>

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Towards reliable forgery detection techniques for remote sensing images: Parametric evaluation and open challenges

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Deepti Patole,
  • Sangita Chaudhari,
  • Tejas Tamkar

摘要

Remote sensing imagery plays a crucial role in diverse applications such as environmental monitoring, urban planning, and disaster response. However, the increasing availability of such data has also raised concerns about remote sensing image integrity. This is a survey article related to forgery detection in remote sensing images, with a focus on copy-move manipulations, which are particularly challenging to identify due to their local nature and high visual similarity to surrounding regions. Our experimentation regarding spectral domain analysis using hyperspectral imagery revealed some detectable anomalies in band-wise reflectance patterns in forged images. The impact of forgery is tested on Ultralytics YOLOv11-based object detection model. The findings underscore the necessity of incorporating robust forgery detection mechanisms before deploying remote sensing data for sensitive applications. Without such measures, forged images may go unnoticed, potentially leading to erroneous interpretations and decisions.