StylePuncher: encoding a hidden QR code into images

Recent advancements in steganography and deep learning have enabled the creation of security methods for imperceptible embedding of data within images. However, many of these methods require substantial time and memory during the training and testing phases. This paper introduces a lighter steganography (also applicable to watermarking purposes) approach, StylePuncher, designed for encoding and decoding 2D binary secret messages within images. The proposed network combines an encoder utilizing neural style transfer techniques with a decoder based on an image-to-image transfer network, offering an efficient and robust solution.

The encoder takes a (512°ø512°ø3) image along with a high-capacity 2D binary message containing 4096 bits (e.g., a QR code or a simple grayscale logo) and ”punches” the message into the cover image. The decoder, trained using multiple weighted loss functions and noise perturbations, then recovers the embedded message. In addition to demonstrating the success of StylePuncher, this paper provides a detailed analysis of the model’s robustness when exposed to various noise perturbations. Despite its lightweight and fast architecture, StylePuncher achieved a notably high decoding accuracy under noisy conditions, outperforming several state-of-the-art steganography models.