Reversible data hiding in encrypted images (RDHEI) enables secret data to be embedded into encrypted images while allowing exact recovery of the original image. However, existing reserving room before encryption (RRBE)-based methods do not fully exploit bit-plane redundancy and generally provide limited support for fine-grained recovery authorization. To address these limitations, this paper proposes a high-capacity RRBE-based RDHEI scheme that integrates dual-stage bit-plane compression with block rearrangement. In addition, a ciphertext-policy attribute-based encryption (CP-ABE)-based key encapsulation mechanism is introduced to support fine-grained and policy-driven image recovery. First, pixel predictors are employed to generate compact prediction errors. Then, consecutive all-zero high-order bit planes are encoded to exploit global sparsity, while the remaining bit planes are further compressed using adaptive hierarchical block variable-length coding (AHBVLC) to capture local structural redundancy. Finally, the compressible bit planes are reorganized, and the image blocks are rearranged according to their embedding capacities. Experiments on BOSSBase, BOWS-2, and UCID demonstrate that the proposed method achieves competitive embedding performance while enabling exact recovery of the original image. The proposed scheme also supports flexible, policy-driven image recovery in multi-user scenarios.
Shen Peng, Minqing Zhang, Yan Ke et al.· Scientific Reports· 0 citations
Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static network optimized for specific channel conditions, their generalization capability across diverse scenarios remains a significant challenge. To address this issue, this paper proposes a novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework. The proposed architecture employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios. The experts are constructed via stacked ResNet blocks and specialize in robust signal detection within specific channel conditions, while the routing mechanism incorporates a lightweight representation learning module, which projects the coarse channel estimate into a low-dimensional latent embedding. The learned embedding characterizes task-relevant channel conditions and provides efficient guidance for accurate expert selection. Link-level simulation experiments demonstrate that the proposed CARNet achieves superior performance across diverse channel conditions.
Chao Jiang, Zhuo Xu, Yongli Yan· 0 citations
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