  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.

Cryptography and Security