Image Data Hiding in Neural Compressed Latent Representations
Chen-Hsiu Huang Ja-Ling Wu
We propose an end-to-end learned image data hiding framework that embeds and
extracts secrets in the latent representations of a generic neural compressor.
By leveraging a perceptual loss function in conjunction with our proposed
message encoder and decoder, our approach simultaneously achieves high image
quality and high bit accuracy. Compared to existing techniques, our framework
offers superior image secrecy and competitive watermarking robustness in the
compressed domain while accelerating the embedding speed by over 50 times.
These results demonstrate the potential of combining data hiding techniques and
neural compression and offer new insights into developing neural compression
techniques and their applications.