RoSteALS：基于自编码器潜空间的鲁棒隐写术
RoSteALS: Robust Steganography using Autoencoder Latent Space
AI总结：
提出RoSteALS隐写技术，利用冻结预训练自编码器潜空间实现轻量、易训练且鲁棒的密钥嵌入，在三个基准上达到完美秘密恢复与可比图像质量，并可适配无载体隐写应用。
摘要：
诸如隐写术和不可见水印等数据隐藏技术在版权保护、隐私保护通信和内容溯源中具有重要应用。现有工作往往在保持图像质量、抵抗扰动的鲁棒性方面存在不足，或训练过于复杂。我们提出RoSteALS，这是一种实用的隐写技术，利用冻结的预训练自编码器将有效载荷嵌入从学习载体图像分布中解放出来。RoSteALS的秘密编码器仅含300k参数，轻量且易于训练，在三个基准上实现了完美的秘密恢复性能和可比的图像质量。此外，RoSteALS可适配于新型无载体隐写应用，其中载体图像可通过去噪扩散过程从噪声中采样或以文本提示为条件生成。我们的模型和代码可在https://github.com/TuBui/RoSteALS获取。
英文摘要：
Data hiding such as steganography and invisible watermarking has important applications in copyright protection, privacy-preserved communication and content provenance. Existing works often fall short in either preserving image quality, or robustness against perturbations or are too complex to train. We propose RoSteALS, a practical steganography technique leveraging frozen pretrained autoencoders to free the payload embedding from learning the distribution of cover images. RoSteALS has a light-weight secret encoder of just 300k parameters, is easy to train, has perfect secret recovery performance and comparable image quality on three benchmarks. Additionally, RoSteALS can be adapted for novel cover-less steganography applications in which the cover image can be sampled from noise or conditioned on text prompts via a denoising diffusion process. Our model and code are available at \url{https://github.com/TuBui/RoSteALS}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
