  Image steganography can hide information in a host image and obtain a stego
image that is perceptually indistinguishable from the original one. This
technique has tremendous potential in scenarios like copyright protection,
information retrospection, etc. Some previous studies have proposed to enhance
the robustness of the methods against image disturbances to increase their
applicability. However, they generally cannot achieve a satisfying balance
between the steganography quality and robustness. In this paper, we focus on
the issue of QR Code steganography that is robust to real-world printing and
photography. Different from common image steganography, QR Code steganography
aims to embed a non-natural image into a natural image and the restored QR Code
is required to be recognizable, which increases the difficulty of data
concealing and revealing. Inspired by the recent developments in
transformer-based vision models, we discover that the tokenized representation
of images is naturally suitable for steganography. In this paper, we propose a
novel QR Code embedding framework, called Printing and Photography Robust
Steganography (PPRSteg), which is competent to hide QR Code in a host image
with unperceivable changes and can restore it even if the stego image is
printed out and photoed. We outline a transition process to reduce the
artifacts in stego images brought by QR Codes. We also propose a steganography
model based on normalizing flow, which combines the attention mechanism to
enhance its performance. To our best knowledge, this is the first work that
integrates the advantages of transformer models into normalizing flow. We
conduct comprehensive and detailed experiments to demonstrate the effectiveness
of our method and the result shows that PPRSteg has great potential in robust,
secure and high-quality QR Code steganography.
