Model Steganography During Model Compression

Researchers have developed a steganographic method (hiding secret data inside another medium) that embeds hidden messages into compressed neural network models (AI systems made smaller through techniques like quantization, pruning, or distillation). The approach allows a receiver with the correct extraction network to recover the hidden data while ordinary users remain unaware it exists, and the method maintains the model's performance in size, speed, and accuracy.