International Journal on Science and Technology

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Multimodal Satellite Forensics Using Cross-Attention Transformers

Author(s) Mr. Piyush Kumar Jha
Country India
Abstract Satellite imagery is increasingly used in critical applications such as monitoring of environment, planning, disaster, and surveillance. However, rise of advanced generative models like Generative Adversarial Networks (GANs) and other image-editing tools, satellite images are exposed to manipulation. It is essential to ensure the authenticity and trustworthiness for decision-making. Multimodal Forensics (MMF) is a field dealing with the integrity of multimedia data. The paper presents a multimodal satellite imagery forensics that utilizes both Electro-Optical (EO) and Synthetic Aperture Radar (SAR) imagery to access the authenticity. The proposed model integrates dual Convolutional Neural Network (CNN) encoders with a Cross-Attention Transformer fusion module to detect tampering based on EO–SAR inconsistency. EO imagery are Panchromatic having high Spatial Resolution whereas SAR imagery has high Spectral Resolution. The model produces authenticity score and tamper heatmap.
Keywords Generative adversarial networks (GANs); Multimodal Forensics (MMF); Electro-Optical (EO); Synthetic Aperture Radar (SAR); Convolutional Neural Network (CNN); Cross Attention Transformer; EO-SAR Consistency; Spatial Resolution; Spectral Resolution; Tamper Detection; Cross-Attention Transformer
Field Computer
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-11-18
DOI https://doi.org/10.71097/IJSAT.v16.i4.9466
Short DOI https://doi.org/hbb8gx
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