RIHOOP: Robust Invisible Hyperlinks in Offline and Online Photographs.

In the era of multimedia and Internet, the quick response (QR) code helps people obtain information from offline to online quickly. However, the QR code is often limited in many scenarios because of its random and dull appearance. Therefore, this article proposes a novel approach to embed hyperlinks into common images, making the hyperlinks invisible for human eyes but detectable for mobile devices equipped with a camera. Our approach is an end-to-end neural network with an encoder to hide messages and a decoder to extract messages. To maintain the hidden message resilient to cameras, we build a distortion network between the encoder and the decoder to augment the encoded images. The distortion network uses differentiable 3-D rendering operations, which can simulate the distortion introduced by camera imaging in both printing and display scenarios. To maintain the visual attraction of the image with hyperlinks, a loss function conforming to the human visual system (HVS) is used to supervise the training of the encoder. Experimental results show that the proposed approach outperforms the previous work on both robustness and quality. Based on the proposed approach, many applications become possible, for example, “image hyperlinks” for advertisement on TV, website, or poster, and “invisible watermark” for copyright protection on digital resources or product packagings. [ABSTRACT FROM AUTHOR]