Satellite image manipulation detection in generative AI era

Matthew W. Chapman; Andrew Tewkesbury; Doreen S. Boyd; Boguslaw Obara; Deepayan Bhowmik

doi:10.1117/12.3033974

13 November 2024 Satellite image manipulation detection in generative AI era

Matthew W. Chapman, Andrew Tewkesbury, Doreen S. Boyd, Boguslaw Obara, Deepayan Bhowmik

Author Affiliations +

Proceedings Volume 13206, Artificial Intelligence for Security and Defence Applications II; 132060S (2024) https://doi.org/10.1117/12.3033974
Event: Security + Defence, 2024, Edinburgh, United Kingdom

Abstract

Generative Artificial Intelligence (AI) is becoming increasingly prevalent due to the availability of machine learning models, such as stable diffusion, and greater computational powers. While this has many advantages, it has led to maliciously generated images being created, and AI-generated satellite imagery is now an emerging threat. The National Geospatial-Intelligence Agency has acknowledged that AI has been utilised to manipulate satellite images for malicious purposes and is not yet widespread. However, there is a high likelihood that it will be, due to the ever-increasing prevalence of social media. This paper proposes the development of a new dataset containing satellite images that have been synthetically manipulated using generative AI models since there are currently none publicly available. We also propose a new deep-learning-based detection algorithm for such manipulation. This research supports the fight against misinformation and will help to ensure that satellite images remain an objective source of truth. The work aims to create a benchmark for detecting manipulated satellite images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Matthew W. Chapman, Andrew Tewkesbury, Doreen S. Boyd, Boguslaw Obara, and Deepayan Bhowmik "Satellite image manipulation detection in generative AI era", Proc. SPIE 13206, Artificial Intelligence for Security and Defence Applications II, 132060S (13 November 2024); https://doi.org/10.1117/12.3033974

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