Deep steganography utilizes the powerful capabilities of deep neural networks to embed and extract messages, but its reliance on an additional message extractor limits its practical use due to the added suspicion it can raise from steganalyzers. To address this problem, we propose StegaINR, which utilizes Implicit Neural Representation (INR) to implement steganography. StegaINR embeds a secret function into a stego function, which serves as both the message extractor and the stego media for secure transmission on a public channel. Recipients need only use a shared key to recover the secret function from the stego function, allowing them to obtain the secret message. Our approach makes use of continuous functions, enabling it to handle various types of messages. To our knowledge, this is the first work to introduce INR into steganography. We performed evaluations on image and climate data to test our method in different deployment contexts.

翻译：深度隐写术利用深度神经网络的强大能力来嵌入和提取消息，但其对额外消息提取器的依赖限制了实际应用，因为这会增加隐写分析者察觉的可疑性。为解决此问题，我们提出StegaINR，利用隐式神经表示实现隐写。StegaINR将秘密函数嵌入到隐写函数中，该隐写函数既充当消息提取器，又作为隐写介质用于在公共信道上安全传输。接收方仅需使用共享密钥即可从隐写函数中恢复秘密函数，从而获取秘密消息。我们的方法利用连续函数，能够处理多种类型的消息。据我们所知，这是首个将隐式神经表示引入隐写术的工作。我们在图像和气候数据上进行了评估，以测试该方法在不同部署场景下的表现。