  Steganography is the process of embedding secret data into another message or
data, in such a way that it is not easily noticeable. With the advancement of
deep learning, Deep Neural Networks (DNNs) have recently been utilized in
steganography. However, existing deep steganography techniques are limited in
scope, as they focus on specific data types and are not effective for
cross-modal steganography. Therefore, We propose a deep cross-modal
steganography framework using Implicit Neural Representations (INRs) to hide
secret data of various formats in cover images. The proposed framework employs
INRs to represent the secret data, which can handle data of various modalities
and resolutions. Experiments on various secret datasets of diverse types
demonstrate that the proposed approach is expandable and capable of
accommodating different modalities.
