Steganography and Steganalysis in Voice over IP: A Review

Wu, Zhijun; Guo, Junjun; Zhang, Chenlei; Li, Changliang

doi:10.3390/s21041032

Cited by 16 publications

(8 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu [1] et al divide the VoIP-based steganography and steganalysis into three categories in the field of voice load: fixed codebook, linear prediction, and pitch delay. Nowadays, there are many studies on these three types of methods, but this article mainly elaborates the steganalysis based on pitch delay.…”

Section: Related Workmentioning

confidence: 99%

“…Each frame contains 4 subframes, and each subframe corresponds to a pitch delay. The structure for any frame , , (1,2,3,4), (1, 2,..., )…”

Section: Pitch Delay Characteristicmentioning

confidence: 99%

“…At present, there are many information carriers for steganography, such as voice, image, video, etc. Steganography enhances the security and confidentiality of communication, but more and more people are beginning to use steganography to do illegal and criminal events [1]. While reviewing the compliance of steganography technology, people also improve the urgency of steganalysis research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AMR Steganalysis based on Adversarial Bi-GRU and Data Distillation

Guo

2022

Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

Self Cite

View full text Add to dashboard Cite

Existing AMR (Adaptive Multi-Rate) steganalysis algorithms based on pitch delay have low detection accuracy on samples with short time or low embedding rate, and the model shows fragility under the attack of adversarial samples. To solve this problem, we design an advanced AMR steganalysis method based on adversarial Bi-GRU (Bi-directional Gated Recurrent Unit) and data distillation. First, Gaussian white noise is randomly added to part of the original speech to form adversarial data set, then artificially annotate a small amount of voice to train the model. Second, perform three transformations of 1.5 times speed, 0.5 times speed, and mirror flip on the remaining original voice data, then put them into Bi-GRU for classification, and the final predicted label obtained by the decision fusion corresponds to the original data. All data with the label is put back into the Bi-GRU model for final training at last. What needs to be pointed out is that each batch of final training data includes normal and adversarial samples. This method adopts a semi-supervised learning method, which greatly saves the resources consumed by manual labeling, and introduces adversarial Bi-GRU, which can realize the two-direction analysis of samples for a long time. Based on improving the detection accuracy, the safety and robustness of the model are greatly improved. The experimental results show that for normal and adversarial samples, the algorithm can achieve accuracy of 96.73% and 95.6% respectively. CCS CONCEPTS•Security and privacy • Network security • Mobile and wireless security.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Each frame contains 4 subframes, and each subframe corresponds to a pitch delay. The structure for any frame , , (1,2,3,4), (1, 2,..., )…”

Section: Pitch Delay Characteristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AMR Steganalysis based on Adversarial Bi-GRU and Data Distillation

Guo

2022

Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

Self Cite

View full text Add to dashboard Cite

show abstract

“…In practical applications, low-bit-rate speech codecs, such as the analysis-by-synthesis linear predictive coding (AbS-LPC), are widely used for compressed speech encoding. Therefore, most steganography methods based on compressed speech utilize AbS-LPC low-bit-rate speech codecs to achieve covert communication [11]. According to the different embedding positions of secret information, these information hiding methods can be mainly summarized as three categories.…”

Section: Introductionmentioning

confidence: 99%

Steganalysis of Compressed Speech Based on Global and Local Correlation Mining

et al. 2022

View full text Add to dashboard Cite

Most of the existing steganalysis methods are designed for specific steganography methods in low-bit-rate compressed speech stream and lack of generalization ability. In practical applications, the steganography methods in compressed speech are various and cannot be predicted in advance. We can only employ numerous possible steganalysis method to detect, which is laborious and time-consuming, and cannot achieve real-time detection. Therefore, it is necessary to develop a general steganalysis method that can detect multiple steganography methods simultaneously for compressed speech. To this end, we propose a steganalysis method based on global and local correlation mining in this paper. Firstly, we introduce a codeword distributed embedding module to transform the compressed codewords into a compact feature representation. Then, we propose global-guided correlation mining module and local-guided correlation mining module to extract the correlation change before and after steganography in the view of global and local. Finally, the detection results can be obtained by the full connection layers. Experimental results show that the proposed method can reach a better detection performance than the existing steganalysis methods at different embedding rates and speech lengths.

show abstract

“…The most commonly used steganography methods based on VoIP are to embed secret information into compressed speech streams by various low-bit-rate speech codecs, including G.723.1, G.729, internet low bit rate codec, and adaptive multi-rate codec [13]. The steganography methods are mainly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Steganalysis of Compressed Speech Based on Association Rule Mining

Gao

Jie

2022

IEEE Access

View full text Add to dashboard Cite

Currently, steganography based on compressed speech streams is gathering more and more attention. Meanwhile, it poses a huge threat to cyber security. As a counter technique, steganalysis can detect whether an illegal secret message is embedded in a compressed speech. To further improve the detection performance of current methods, a novel steganalysis method based on codeword association rule mining (CARM) is proposed in this paper. Firstly, we analyzed the spatiotemporal relationships between codewords in compressed speech. Secondly, the steganography-sensitivity codeword association rule base in training set was built based on the confidence change of codeword association rules before and after steganography. Thirdly, the steganography characteristic index and the corresponding dynamic partition threshold in validation set were computed to determine whether the compressed speech segment contains covert communication or not. Finally, comprehensive experiments were conducted to evaluate the performance of the proposed CARM steganalysis method under various conditions, including different association rule patterns, whether to use dynamic partition threshold, different embedding rates, different speech lengths, etc. The experimental results verify that CARM can achieve better performance than the comparison methods. In addition, the detection accuracy of CARM method can be improved significantly by using dynamic partition threshold at low embedding rates.

show abstract

Steganography and Steganalysis in Voice over IP: A Review

Cited by 16 publications

References 83 publications

AMR Steganalysis based on Adversarial Bi-GRU and Data Distillation

AMR Steganalysis based on Adversarial Bi-GRU and Data Distillation

Steganalysis of Compressed Speech Based on Global and Local Correlation Mining

Steganalysis of Compressed Speech Based on Association Rule Mining

Contact Info

Product

Resources

About