Implicit Steganography Beyond the Constraints of Modality
This addresses the problem of secure and efficient information hiding across different data types for applications in privacy and security, representing a novel advancement rather than an incremental improvement.
The paper tackles the challenge of cross-modal steganography by proposing INRSteg, a framework based on Implicit Neural Representations that hides secret information across diverse modalities like image, audio, video, and 3D shape, achieving flexibility, security, and robustness in experiments.
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.