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Deep hiding, concealing secret information using Deep Neural Networks (DNNs),can significantly increase the embedding rate and improve the efficiency ofsecret sharing. Existing works mainly force on designing DNNs with higherembedding rates or fancy functionalities. In this paper, we want to answer somefundamental questions: how to increase and what determines the embedding rateof deep hiding. To this end, we first propose a novel Local Deep Hiding (LDH)scheme that significantly increases the embedding rate by hiding large secretimages into small local regions of cover images. Our scheme consists of threeDNNs: hiding, locating, and revealing. We use the hiding network to convert asecret image in a small imperceptible compact secret code that is embedded intoa random local region of a cover image. The locating network assists therevealing process by identifying the position of secret codes in the stegoimage, while the revealing network recovers all full-size secret images fromthese identified local regions. Our LDH achieves an extremely high embeddingrate, i.e., $16\times24$ bpp and exhibits superior robustness to common imagedistortions. We also conduct comprehensive experiments to evaluate our schemeunder various system settings. We further quantitatively analyze the trade-offbetween the embedding rate and image quality with different image restorationalgorithms.

Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding