Deep Cross-Modal Steganography Using Neural Representations
This addresses the limitation of existing deep steganography techniques that are not effective for cross-modal applications, offering a more versatile solution for secure data hiding.
The paper tackles the problem of cross-modal steganography by proposing a framework using Implicit Neural Representations (INRs) to hide secret data of various formats in cover images, demonstrating expandability and capability across different modalities in experiments.
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.