Neural network–based steganography has garnered considerable attention for its strong security. However, existing approaches often suffer from excessive coupling between the embedding and extraction networks: the sender and receiver must employ paired models and maintain strict synchronization. Such synchronization not only complicates deployment but also introduces more severe potential risks of information leakage. To overcome this limitation, we propose a synchronization-free steganographic framework based on decoupled neural embedding networks, following the destruction–restoration principle. In our design, message embedding is realized through a destruction operation, while recovery is achieved using a neural network from the audio restoration domain. This decoupled architecture allows the sender to upgrade, replace, or randomize the embedding network—thus enabling dynamic model changes—without impairing the receiver’s ability to correctly extract the hidden message. As a result, synchronization-related vulnerabilities are fundamentally eliminated. Experimental results demonstrate that even under dynamic changes in the embedding network, the hidden information can still be reliably extracted, confirming both the effectiveness and enhanced security of the proposed approach.

With the rapid advancement of audio generation models, research on audio steganography has entered a new phase of opportunity. Nevertheless, most existing generative steganographic approaches focus primarily on security while neglecting the compression and transcoding processes that are common in real-world communication. This oversight leads to two major issues: the introduction of verification mechanisms would violate its security proof assumptions, and quantization-based compression markedly reduces message extraction accuracy. In this work, we propose a robust audio steganography method that preserves provable security under various compression conditions. The security of our method relies exclusively on the latent space following a fixed distribution, which is independent of the embedded message. The proposed encoding–decoding scheme supports a tunable trade-off between capacity and robustness, allowing the sacrifice of partial capacity to reinforce robustness. Theoretical analysis shows that even with redundant error-checking codes, the latent distribution remains invariant after message embedding, thereby preserving both steganographic security and generation quality while ensuring practical applicability. Experimental results demonstrate that our method maintains message extraction accuracy under both MP3 and AAC compression and re-compression across bitrates of 160 kbps, 128 kbps, 64 kbps, 48 kbps, and even 32 kbps.