StegaStamp: Invisible Hyperlinks in Physical Photographs

TL;DR

This paper introduces StegaStamp, a neural network-based steganographic system that invisibly embeds and robustly retrieves hyperlinks in physical photographs, enabling secure, imperceptible digital data access through imaging systems.

Contribution

The paper presents a novel learned steganographic algorithm capable of embedding robust hyperlinks in photos that withstand real-world distortions, with a prototype demonstrating real-time decoding.

Findings

Successfully encodes 56-bit hyperlinks with error correction.

Robustly decodes hyperlinks from in-the-wild videos with lighting and perspective variations.

Achieves near-invisible embedding in physical photographs.

Abstract

Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction - sufficient to embed a unique code within every photo on the internet.