Steganography based on texts has always been a hot but extremely hard research topic Due to the high coding characteristics of the text compared to other information carriers, the redundancy of information is very low, which makes it really difficult to hide information inside In this paper, combined with the recurrent neural network (RNN) and reinforcement learning (RL), we designed and implemented a real-time interactive text steganography model (RITS) The proposed model can automatically generate semantically coherent and syntactically correct dialogues based on the input sentence, through the reasonable encoding of the text in the dialog generation process to realize secret information hiding and transmission We trained our model using publicly collected datasets which contains 5808 dialogues and evaluated the proposed model from several perspectives Experimental results show that the proposed model can be very efficient to implement the embedding and extraction of information The generated dialogue texts are of high quality which shows high concealment

RITS: Real-Time Interactive Text Steganography Based on Automatic Dialogue Model

Using low-rate compressed speech coding for large-capacity steganography is always a big challenge due to its low redundant information. To overcome this challenge, we propose a method of embedding and extracting steganography in low-rate speech coding using three-dimensional Sudoku matrix. Analysis shows that this method can enhance the concealment of steganographic information and improve the steganography capacity of low-rate speech coding. The experimental results showed that using the current typical low speech coding standard G.723.1 achieved a steganographic capacity of 200 bit/s and a reduction of less than 10% in the sensory evaluation value of the speech quality of the coded speech.

A Sudoku Matrix-Based Method of Pitch Period Steganography in Low-Rate Speech Coding

Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows. In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multi channel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolution layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not.We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows.

SILK, as a speech codec for real-time packet-based voice communications, which is widely used in many popular mobile Internet application, such as Skype, WeChat, QQ, WhatsApp, etc. It will be a novel and ideal carrier for information hiding. In this paper, a secure steganography scheme for SILK is proposed, which embeds secret message by modifying the LSF (Line Spectral Frequency) quantization indices based on the statistical distribution of LSF Codebook. The experimental results show that the auditory concealment of the proposed scheme is excellent, the decrease in PESQ is very small. The average hiding capacity can achieve 129 bps and 223 bps under the sampling rate of 8 kHz and 16 kHz respectively. More importantly, the proposed scheme has good statistical security. In this scheme, the statistical distribution of LSF Codebook is considered as a constraint condition to make the distribution of stego's codeword close to that of the cover audio. Under the steganlysis scheme which is referenced from the existing steganlysis scheme for G.723.1, the average correct detection rate is under 55.4% for both cover and stego audio. To the best of our knowledge, this is the first work to hide information in SILK. Based on the similar principle of speech compression, the method can be extended to other CELP codec, such as G.723.1, G.729, AMR, etc.

SILK Steganography Scheme Based on the Distribution of LSF Parameter

Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. The existing steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for one certain category of steganography methods, thus lacking generalization capability. In this paper, a common method for detecting multiple steganographies in low-bit-rate compressed speech based on a code element Bayesian network is proposed. In an AbS-LPC low-bit-rate compressed speech stream, spatiotemporal correlations exist between the code elements, and steganography will eventually change the values of these code elements. Thus, the method presented in this paper is developed from the code element perspective. It consists of constructing a code element Bayesian network based on the strong correlations between code elements, learning the network parameters by utilizing a Dirichlet distribution as the prior distribution, and finally implementing steganalysis based on Bayesian inference. Experimental results demonstrate that the proposed method performs better than the existing steganalysis methods for detecting multiple steganographies in the AbS-LPC low-bit-rate compressed speech.

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A Common Method for Detecting Multiple Steganographies in Low-Bit-Rate Compressed Speech Based on Bayesian Inference

Redundant information of low-bit rate speech is extremely small, thus it's very difficult to implement large capacity steganography on the low-bit rate speech. Based on multiple vector quantization characteristics of the Line Spectrum Pair (LSP) of the speech codec, this paper proposes a steganography scheme using a 3D-sudoku matrix to enlarge capacity and improve quality of speech. A cyclically moving algorithm to construct 3D-Sudoku matrix for steganography is proposed in this paper, as well as an embedding and an extracting algorithm of steganography based on 3D-Sudoku matrix in low-bit rate speech codec. Theoretical analysis is provided to demonstrate that the concealment and the hidden capacity are greatly improved with the proposed scheme. Experimental results show the hidden capacity is raised to 200bps in ITU-T G.723.1 codec. Moreover, the quality of steganography speech in Perceptual Evaluation of Speech Quality (PESQ) reduces no more than 4%, indicating little impact on the quality of speech.

Xueshun Peng

Papers

A steganography scheme in a low-bit rate speech codec based on 3D-sudoku matrix