隐藏和说话：面向语音隐写的深度神经网络
Hide and Speak: Towards Deep Neural Networks for Speech Steganography
摘要：
隐写术是一门将秘密信息隐藏在普通公共信息中的科学，这被称为载体。传统上，数字信号处理技术，如最低有效位编码，用于隐藏消息。在本文中，我们探索使用深度神经网络作为语音数据的隐写功能。我们证明了为视觉提出的隐写模型不太适合语音，并提出了一种新的模型，该模型包括短时傅立叶变换和逆短时傅立叶变换作为网络内的可微层，从而对网络输出施加了重要的约束。通过与基于多个语音数据集的深度学习的对比实验，验证了该方法的有效性，并对实验结果进行了定量和定性分析。此外，我们表明，所提出的方法可以适用于隐藏多个消息在一个单一的载波使用多个解码器或一个条件解码器。最后，我们评估了我们的模型在不同的信道失真。定性实验表明，对载体的修改是不明显的人类听众和解码的消息是高度可理解的。
英文摘要：
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, such as least significant bit encoding, were used for hiding messages. In this paper, we explore the use of deep neural networks as steganographic functions for speech data. We showed that steganography models proposed for vision are less suitable for speech, and propose a new model that includes the short-time Fourier transform and inverse-short-time Fourier transform as differentiable layers within the network, thus imposing a vital constraint on the network outputs. We empirically demonstrated the effectiveness of the proposed method comparing to deep learning based on several speech datasets and analyzed the results quantitatively and qualitatively. Moreover, we showed that the proposed approach could be applied to conceal multiple messages in a single carrier using multiple decoders or a single conditional decoder. Lastly, we evaluated our model under different channel distortions. Qualitative experiments suggest that modifications to the carrier are unnoticeable by human listeners and that the decoded messages are highly intelligible.
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。
