Learning Invisible Markers for Hidden Codes in Offline-to-online Photography
Jun Jia, Zhongpai Gao, Dandan Zhu, Xiongkuo Min, Guangtao Zhai, Xiaokang Yang
Abstract
QR (quick response) codes are widely used as an offline-to-online channel to convey information (e.g., links) from publicity materials (e.g., display and print) to mobile devices. However, QR codes are not favorable for taking up valuable space of publicity materials. Recent works propose invisible codes/hyperlinks that can convey hidden information from offline to online. However, they require markers to locate invisible codes, which fails the purpose of invisible codes to be visible because of the markers. This paper proposes a novel invisible information hiding architecture for display/print-camera scenarios, consisting of hiding, locating, correcting, and recovery, where invisible markers are learned to make hidden codes truly invisible. We hide information in a sub-image rather than the entire image and include a localization module in the end-to-end framework. To achieve both high visual quality and high recovering robustness, an effective multi-stage training strategy is proposed. The experimental results show that the proposed method outperforms the state-of-the-art information hiding methods in both visual quality and robustness. In addition, the automatic localization of hidden codes significantly reduces the time of manually correcting geometric distortions for photos, which is a revolutionary innovation for information hiding in mobile applications.