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.

翻译：隐写术是将秘密数据嵌入另一消息或数据中，使其不易被察觉的过程。随着深度学习的进步，深度神经网络（DNNs）最近已被应用于隐写术。然而，现有深度隐写技术存在局限性，因为它们专注于特定数据类型，对跨模态隐写效果不佳。因此，我们提出一种使用隐式神经表征（INRs）的深度跨模态隐写框架，用于将多种格式的秘密数据隐藏于载体图像中。该框架利用INRs对秘密数据进行表征，可处理不同模态和分辨率的数据。在多种类型的不同秘密数据集上的实验表明，所提方法具有良好的可扩展性，能够适配不同模态。