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Digital media protection text steganography method based on variational automatic encoder

A digital media protection and autoencoder technology, applied in the field of information security, can solve the problems of limited text usage scenarios, uncontrollable text content, unsupervised and other problems, and achieve the effect of visual indistinguishability

Pending Publication Date: 2022-01-28
CHONGQING UNIV OF POSTS & TELECOMM
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  • Application Information

AI Technical Summary

Problems solved by technology

The text steganography model is mostly trained based on a corpus with a single attribute. It is an unsupervised steganographic text generation model, resulting in uncontrollable text content and limited usage scenarios for the generated text.

Method used

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  • Digital media protection text steganography method based on variational automatic encoder
  • Digital media protection text steganography method based on variational automatic encoder
  • Digital media protection text steganography method based on variational automatic encoder

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Embodiment 1

[0069] In this embodiment, the digital media protection text steganography method based on variational autoencoder is as follows figure 1 , the global keywords, long sequences and secret information to be hidden are taken as input data packets, and the output after the steganographic text generation model is steganographic text embedded with secret information. Specifically, it includes the following four steps:

[0070] S1: Data preprocessing. In the data preprocessing part, it is necessary to extract the global keywords and group keywords of the training text, and it is necessary to divide a long sequence into multiple short sequences, and each short sequence corresponds to a group of keywords, and the global keywords are all A union of group keywords. If you want to embed secret information in plain text, you also need to encode the secret information into a bit stream.

[0071] S2: Extract textual context relevance. First of all, it is necessary to process the text int...

Embodiment 2

[0075] This embodiment further explains the data preprocessing of step S1 in embodiment 1. This step is mainly divided into the following four steps:

[0076] S11: Sequence word segmentation. Usually, keywords cannot be obtained directly from the original sequence. Words that have nothing to do with part of speech usually affect the accuracy of keywords. Stop words are removed from the word segmentation results.

[0077] S12: acquisition of global keywords and group keywords. Group keywords are mainly used for local control of text generation content, and mature keyword extraction tools are usually used to obtain text keywords; global keywords are the union of group keywords.

[0078] S13: long sequence division. Using long sequences for text generation often loses relevant contextual features in the second half of the text, and may lead to uncontrollable content of text generation. Therefore, it is necessary to divide the long text into multiple short sequences, and then t...

Embodiment 3

[0104] This embodiment further describes the neural network model constructed by the present invention. The model is mainly divided into three stages: encoding network, Gaussian sampling, and decoding network. The first stage of the encoding network is mainly to fuse global keywords and long sequences to obtain its global feature representation; the second stage of Gaussian sampling is mainly used to perform Gaussian sampling on the global features in the encoding network, and the sampling results obey the isotropic Gaussian distribution; the second stage The three-stage decoding network mainly decodes the sampling results, and then realizes the steganography of the text according to the conditional probability distribution of the text and combined with the corresponding encoding algorithm. For the encoding network, Gaussian sampling, and decoding network, it specifically includes the following steps:

[0105] S31: Coding Network

[0106] The encoding network is divided into ...

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Abstract

The invention belongs to the field of information security, and particularly relates to a digital media protection text steganography method based on a variational automatic encoder. The method comprises the following steps: constructing a neural network model consisting of an encoding network, Gaussian sampling and a decoding network, and vectorizing a text; utilizing the encoding network to respectively obtain features of global keywords and a long sequence, and fusing the features of the global keyword and the long sequence to obtain global feature representation; carrying out Gaussian sampling on the global feature representation in the encoding network by using Gaussian sampling; decoding a sampling result of Gaussian sampling by using the decoding network to obtain conditional probability distribution of the text; selecting k words with the maximum conditional probability, selecting a word corresponding to a secret bit stream through Huffman coding, and completing steganography of a file. According to the method, long and diverse steganographic texts can be generated, so that the steganographic texts can carry more secret information, and visual indistinguishability, statistical indistinguishability, and semantic indistinguishability of natural languages and the steganographic texts are realized.

Description

technical field [0001] The invention belongs to the field of information security, in particular to a digital media protection text steganography method based on a variational automatic encoder. Background technique [0002] The research on information hiding technology originated from foreign countries. After the successful holding of the National Information Hiding and Multimedia Information Security Academic Conference in 1999, it gradually flowed into China and became a new research field. In information hiding technology, steganography and digital watermarking are used to solve security problems such as covert communication, digital forensics and copyright protection. Steganography is one of the key technologies in information hiding. Its essence is to embed secret information into the carrier data to hide the existence of communication, so that the attacker cannot know whether the information contains secret information from the appearance. Digital watermarking refers...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F40/279G06F40/30G06N3/04G06N3/08
CPCG06F16/3344G06F16/3346G06F40/279G06F40/30G06N3/04G06N3/08
Inventor 刘红李政肖云鹏李暾贾朝龙王蓉
Owner CHONGQING UNIV OF POSTS & TELECOMM
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