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NRGMark: Localized Watermarking for Energy Transparency in Images
Journal article

NRGMark: Localized Watermarking for Energy Transparency in Images

Shruti Agarwal, Elie Michel, Vishal Asnani, Tania Mathern and John Collomosse
2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2026), pp.7779-7788
IEEE Workshop on Applications of Computer Vision (WACV)
IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV 2026) (Tucson, Arizona, USA, 06/03/2026–10/03/2026)
05/05/2026

Abstract

Communication systems Computer networks Digital images Location awareness Mobile communication Payloads Pixel Space technology Videos Military Aircraft
We present NRGMark, a region-based image watermarking framework to embed provenance metadata into composite graphic designs such as posters. NRGMark enables imperceptible watermarking of distinct visual elements each carrying independent metadata on aspects like environmental impact, such as the energy consumption associated with generative AI (GenAI) use. NRGMark extends image watermark encoder-decoder models by incorporating an object localization network to detect and decode multiple watermarked regions within a document, even under image transformations and physical print-scan degradation. NRGMark interoperates with several watermarking techniques and the emerging C2PA open standard for media provenance to encode environmental impact metadata. We demonstrate NRGMark on both synthetic and real-world design layouts, illustrating its potential to support energy transparency in the age of GenAI.
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Agarwal-WACV-20262.24 MBDownloadView
Author's Accepted Manuscript Embargo until publication date CC BY V4.0
url
https://doi.org/10.1109/WACV61042.2026.00751View
Published (Version of record)
url
https://wacv.thecvf.com/Conferences/2026View
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