论文标题
深层残留神经网络，用于语音隐志图像
Deep Residual Neural Networks for Image in Speech Steganography
论文作者
论文摘要
隐肌是将秘密信息隐藏在公开可见的载体信息中的艺术。理想情况下，它是在不修改载体的情况下完成的，并且秘密消息中信息丢失最小。最近，已将基于深度学习的模拟方法应用于不同的消息类型。我们提出了一种基于深度学习的技术，以在有限长度的语音段内隐藏源RGB图像消息而不会感知损失。为了实现这一目标，我们训练三个神经网络；一个编码网络将消息隐藏在运营商中，该网络是一个解码网络，可从运营商和附加的图像增强器网络重建消息，以进一步改善重建的消息。我们还讨论了提出的算法的未来改进。
Steganography is the art of hiding a secret message inside a publicly visible carrier message. Ideally, it is done without modifying the carrier, and with minimal loss of information in the secret message. Recently, various deep learning based approaches to steganography have been applied to different message types. We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss. To achieve this, we train three neural networks; an encoding network to hide the message in the carrier, a decoding network to reconstruct the message from the carrier and an additional image enhancer network to further improve the reconstructed message. We also discuss future improvements to the algorithm proposed.
