Aparecium: Revealing Secrets from Physical Photographs
Zhe Lei, Jie Zhang, Jingtao Li, Weiming Zhang, and Nenghai Yu

TL;DR
Aparecium is a deep learning-based watermarking framework that robustly encodes and retrieves watermarks from physical photographs under complex distortions like folding, bending, and shooting from various angles.
Contribution
The paper introduces Aparecium, a novel deep watermarking method that handles complex physical distortions and irregular shapes, surpassing limitations of existing solutions.
Findings
Robust against digital and physical distortions including folding and bending
Effective at long-distance and large-angle photography
Maintains high visual quality of watermarked images
Abstract
Watermarking is a crucial tool for safeguarding copyrights and can serve as a more aesthetically pleasing alternative to QR codes. In recent years, watermarking methods based on deep learning have proved superior robustness against complex physical distortions than traditional watermarking methods. However, they have certain limitations that render them less effective in practice. For instance, current solutions necessitate physical photographs to be rectangular for accurate localization, cannot handle physical bending or folding, and require the hidden area to be completely captured at a close distance and small angle. To overcome these challenges, we propose a novel deep watermarking framework dubbed \textit{Aparecium}. Specifically, we preprocess secrets (i.e., watermarks) into a pattern and then embed it into the cover image, which is symmetrical to the final…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Face recognition and analysis
