Steganography, as one of the three basic information security systems, has
long played an important role in safeguarding the privacy and confidentiality
of data in cyberspace. The text is the most widely used information carrier in
people's daily life, using text as a carrier for information hiding has broad
research prospects. However, due to the high coding degree and less information
redundancy in the text, it has been an extremely challenging problem to hide
information in it for a long time. In this paper, we propose a steganography
method which can automatically generate steganographic text based on the Markov
chain model and Huffman coding. It can automatically generate fluent text
carrier in terms of secret information which need to be embedded. The proposed
model can learn from a large number of samples written by people and obtain a
good estimate of the statistical language model. We evaluated the proposed
model from several perspectives. Experimental results show that the performance
of the proposed model is superior to all the previous related methods in terms
of information imperceptibility and information hidden capacity.

本文提出了一种基于马尔可夫链模型和哈夫曼编码的隐写术方法，它可以自动生成流畅的文本承载，该模型可以从大量的样本中学习并获得良好的统计语言模型，实验结果表明，该模型的性能优于所有以前相关方法。

基于马尔科夫模型和霍夫曼编码自动生成隐写文本

Automatically Generate Steganographic Text Based on Markov Model and  Huffman Coding

Neural networks are both computationally intensive and memory intensive,
making them difficult to deploy on embedded systems with limited hardware
resources. To address this limitation, we introduce "deep compression", a three
stage pipeline: pruning, trained quantization and Huffman coding, that work
together to reduce the storage requirement of neural networks by 35x to 49x
without affecting their accuracy. Our method first prunes the network by
learning only the important connections. Next, we quantize the weights to
enforce weight sharing, finally, we apply Huffman coding. After the first two
steps we retrain the network to fine tune the remaining connections and the
quantized centroids. Pruning, reduces the number of connections by 9x to 13x;
Quantization then reduces the number of bits that represent each connection
from 32 to 5. On the ImageNet dataset, our method reduced the storage required
by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method
reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of
accuracy. This allows fitting the model into on-chip SRAM cache rather than
off-chip DRAM memory. Our compression method also facilitates the use of
complex neural networks in mobile applications where application size and
download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU,
compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy
efficiency.

该论文介绍了一种名为 “深度压缩” 的技术，通过三阶段的网络压缩流程 —— 剪枝、量化和霍夫曼编码 —— 可以将神经网络模型的存储资源需求减小 35 到 49 倍，而同时不影响网络的准确性，该技术可以在嵌入式系统中使用，可以极大地提升模型的应用性能。