Adaptive PUPM-Based HEVC Video Steganography Balancing Embedding Performance and Security

For the prediction unit partition modes (PUPM)-based steganography, a mainstream branch of high efficiency video coding (HEVC) video steganography, striking a balance between embedding performance and security is very challenging. Including the $2\mathcal {N} \times 2\mathcal {N}$ PUPMs having the maximum number of PUPMs into data embedding is indeed an effective way of enlarging the embedding capacity, but it necessarily causes a significant decline in security. Therefore, a multi-factor-involved cost function (MFICF) is proposed in this paper to evaluate the embedding cost for modifying each PUPM by comprehensively considering four different aspects affecting the embedding performance and security. With the assistance of MFICF, the 7-ary notational system is combined to use all the 7 types of PUPMs containing $2\mathcal {N} \times 2\mathcal {N}$ for data embedding, thus enlarging the embedding capacity as well as enhancing the embedding efficiency. The syndrome-trellis code driven by MFICF, named CFSTC, is designed to preferentially select PUPMs with low embedding costs for data embedding, so that the embedding efficiency is largely enhanced. The security is effectively guaranteed by allocating a large embedding cost for modifying $2\mathcal {N} \times 2\mathcal {N}$ to another type of PUPM. Finally, a lightweight convolutional neural network in combination with gated channel transformation, called GSCNet, is proposed to replace the in-loop filter in HEVC, further optimizing the visual distortion and bitrate increase caused by data embedding. Combining these components above, we design a PUPM-based steganography algorithm, GSAPM. Experimental results show that GSAPM effectively enhances the embedding performance while maintaining high security.