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 medium for secure transmission on a public channel. Recipients only need to use a shared key to recover the secret function from the stego function, allowing them to obtain the secret message. Our approach employs continuous functions, enabling it to handle various types of messages. To our knowledge, this is the first work to introduce INR into steganography. We perform evaluations on image, climate data, and Neural Radiance Field (NeRF) synthetic dataset to test our method in different deployment contexts.