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.

翻译：我们提出了一种端到端学习的图像数据隐藏框架，该框架在通用神经压缩器的潜在表示中嵌入和提取秘密信息。通过结合感知损失函数与我们提出的消息编码器和解码器，该方法同时实现了高图像质量和高比特精确度。与现有技术相比，本框架在压缩域中提供了更优的图像保密性和具有竞争力的水印鲁棒性，同时将嵌入速度提升了50倍以上。这些结果展示了数据隐藏技术与神经压缩相结合的潜力，并为开发神经压缩技术及其应用提供了新的见解。