Static deep neural network (DNN) watermarking embeds watermarks into the
weights of DNN model by irreversible methods, but this will cause permanent
damage to watermarked model and can not meet the requirements of integrity
authentication. For these reasons, reversible data hiding (RDH) seems more
attractive for the copyright protection of DNNs. This paper proposes a novel
RDH-based static DNN watermarking method by improving the non-reversible
quantization index modulation (QIM). Targeting the floating-point weights of
DNNs, the idea of our RDH method is to add a scaled quantization error back to
the cover object. Two schemes are designed to realize the integrity protection
and legitimate authentication of DNNs. Simulation results on training loss and
classification accuracy justify the superior feasibility, effectiveness and
adaptability of the proposed method over histogram shifting (HS).

本文提出了一种基于可逆数据隐藏的静态深度神经网络水印方法，通过对量化索引调制进行改进，将缩放的量化误差添加回覆盖对象以实现 DNN 的完整性保护和合法鉴定。

可逆深度神经网络数字水印技术：匹配浮点数权重

Reversible Deep Neural Network Watermarking:Matching the Floating-point  Weights

Extracting correlation features between codes-words with high computational
efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this
paper, we utilized attention mechanisms, which have recently attracted enormous
interests due to their highly parallelizable computation and flexibility in
modeling correlation in sequence, to tackle steganalysis problem of
Quantization Index Modulation (QIM) based steganography in compressed VoIP
stream. We design a light-weight neural network named Fast Correlation Extract
Model (FCEM) only based on a variant of attention called multi-head attention
to extract correlation features from VoIP frames. Despite its simple form, FCEM
outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional
Neural Networks (CNNs) models on both prediction accuracy and time efficiency.
It significantly improves the best result in detecting both low embedded rates
and short samples recently. Besides, the proposed model accelerates the
detection speed as twice as before when the sample length is as short as 0.1s,
making it a excellent method for online services.

本研究利用关注机制处理定量化指数调制压缩 VoIP 流中基于 QIM 的隐写分析问题。研发了一个名为快速相关提取模型（FCEM）的轻量级神经网络来提取 VoIP 帧中的相关特征，具有比复杂的循环神经网络和卷积神经网络更高的准确性和时间效率。此外，提出的模型在样品长度为 0.1s 时可将检测速度提高一倍，适用于在线服务。