We propose an end-to-end learned image data hiding framework that embeds and
extracts secrets in the latent representations of a generic neural compressor.
By leveraging a perceptual loss function in conjunction with our proposed
message encoder and decoder, our approach simultaneously achieves high image
quality and high bit accuracy. Compared to existing techniques, our framework
offers superior image secrecy and competitive watermarking robustness in the
compressed domain while accelerating the embedding speed by over 50 times.
These results demonstrate the potential of combining data hiding techniques and
neural compression and offer new insights into developing neural compression
techniques and their applications.

我们提出了一个端到端的学习图像数据隐藏框架，通过在通用神经压缩器的潜在表示中嵌入和提取秘密信息。通过结合我们提出的消息编码器和解码器以及感知损失函数，我们的方法同时实现了高质量和高比特准确性的图像。与现有技术相比，我们的框架在压缩领域提供了优秀的图像保密性和竞争性的水印鲁棒性，而且嵌入速度加快了 50 倍以上。这些结果展示了组合数据隐藏技术和神经压缩的潜力，并为开发神经压缩技术及其应用提供了新的见解。