Model Steganography During Model Compression

Summary

Researchers propose a steganographic method that embeds secret data into neural network models during compression, covering quantization, pruning and distillation. A separate extraction network lets a receiver recover the hidden data while ordinary users remain unaware of it. The paper reports reduced model size and inference time, high accuracy, and evaluates capacity, security and robustness for steganography.

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