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Learning Invisible Markers for Hidden Codes in Offline-to-Online Photography

CVPR 2022 · Image generation › Image restoration · approach: Reflection removal · 38 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Learn invisible markers and localize the encoded sub-image automaticallyOurs · Geometric distortion [-20,+20] to [-40,+40]BER: 2.04%, 4.01%, 4.74%, 7.53%, 13.77% vs StegaStamp 14.45%, 24.10%, 27.85%, 32.15%, 36.27% and RIHOOP 48.20%, 51.30%, 48.90Helped
Hide information in a sub-image and train an end-to-end localization moduleOurs · Test imagesPSNR: 32.95 vs 29.82 and 35.86Mixed
Use the localization network instead of manual correction (ablation)Ours · Photograph preprocessingtime: 0.014s vs 7sHelped
Use the localization network instead of manual correction (ablation)Ours · Distortion 1 and Distortion 2BER: 0.16% -> 2.10%; 1.22% -> 7.57%Hurt
Add the edge gain loss (ablation)Ours · Test imagesIoU: 0.8866 -> 0.9318Helped
Add the edge gain loss (ablation)Ours · Test imagesBER: 11.29% -> 8.08%Helped
Add the edge gain loss (ablation)Ours · Test imagesPSNR: 40.64 -> 41.87Helped
Hide information in high-frequency regions instead of low-frequency regions (ablation)Ours · Different sub-image selectionsBER: BER increases by 9.35%Hurt
Train with a multi-stage strategy rather than optimizing all losses at once (ablation)Ours · TrainingconvergenceHelped

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