Implicit Steganography Beyond the Constraints of Modality

TL;DR

This paper introduces INRSteg, a novel cross-modal steganography framework using Implicit Neural Representations that enables hiding data across diverse modalities with reduced cost and increased security.

Contribution

INRSteg is the first framework to incorporate diverse modalities for both secret and cover data, eliminating the need for training neural networks and enhancing flexibility and robustness.

Findings

Successfully hides data across image, audio, video, and 3D shapes.

Reduces memory and computational costs compared to traditional methods.

Demonstrates high security and robustness in extreme modality settings.

Abstract

Cross-modal steganography is committed to hiding secret information of one modality in another modality. Despite the advancement in the field of steganography by the introduction of deep learning, cross-modal steganography still remains to be a challenge to the field. The incompatibility between different modalities not only complicate the hiding process but also results in increased vulnerability to detection. To rectify these limitations, we present INRSteg, an innovative cross-modal steganography framework based on Implicit Neural Representations (INRs). We introduce a novel network allocating framework with a masked parameter update which facilitates hiding multiple data and enables cross modality across image, audio, video and 3D shape. Moreover, we eliminate the necessity of training a deep neural network and therefore substantially reduce the memory and computational cost and avoid domain adaptation issues. To the best of our knowledge, in the field of steganography, this is the first to introduce diverse modalities to both the secret and cover data. Detailed experiments in extreme modality settings demonstrate the flexibility, security, and robustness of INRSteg.