Due to the exponential increase in cyber/online crimes involving the use of steganography in streaming media, it is crucial to focus on studying steganalysis technology for the detection of covert communications. Several existing steganalysis methods rely on classifiers such as Support Vector Machines (SVM) and conventional statistical analysis. However, these methods suffer from high computational intensity and time consumption, making them unsuitable for real-time detection of steganography in streaming media. Additionally, some steganalysis techniques are specifically designed for certain codecs or depend on particular steganographic algorithms, limiting their practicality and universality. To address these challenges, this study devises five steganalysis algorithms tailored for Voice over Internet Protocol (VoIP) to detect covert channels in streaming media communications. The algorithms are as follows: ANOVA, derivative-based Mel Frequency Cepstrum Coefficient (DMFCC), improved Regular and Singular (RS) test, Mann–Whitney–Wilcoxon (M-W-W) test, and FFT-based Steganalysis. Experimental evaluations were performed on two sets of media streams — one without steganography and the other with steganography. The results obtained from these experiments reveal that the proposed VoIP steganalysis algorithms exhibit variations in terms of detection sensitivity, whether blind or targeted, online or offline, as well as the requirement for original samples.

Abstract Deep learning techniques have shown promise in audio steganalysis, which involves detecting the presence of hidden information (steganography) in audio files. However, deep learning models are prone to overfitting, particularly when there is limited data or when the model architecture is too complex relative to the available data for VoIP steganography. To address these issues, new deep‐learning approaches need to be explored. In this study, a new convolutional neural network for audio steganalysis, incorporating a multi‐scale feature fusion method and an attention mechanism, was devised to enhance the detection of steganographic content in audio signals encoded with G729a. To improve the network's adaptability, a multi‐scale parallel multi‐branch architecture was employed, allowing characteristic signals to be sampled with varying granularities and adjusting the receptive field effectively. The attention mechanism enables weight learning on the feature information after multi‐scale processing, capturing the most relevant information for steganalysis. By combining multiple feature representations using a weighted combination, the deep learning model's performance was significantly enhanced. Through rigorous experimentation, an impressive accuracy rate of 94.55% was achieved in detecting malicious steganography. This outcome demonstrates the efficacy of the proposed neural network, leveraging both the multi‐scale feature fusion method and the attention mechanism.

Voice over IP (VoIP) is finding its way into several applications, but its security concerns still remain. This paper shows how a new self-adaptive steganographic method can ensure the security of covert VoIP communications over the Internet. In this study an Active Voice Period Detection algorithm is devised for PCM codec to detect whether a VoIP packet carries active or inactive voice data, and the data embedding location in a VoIP stream is chosen randomly according to random sequences generated from a logistic chaotic map. The initial parameters of the chaotic map and the selection of where to embed the message are negotiated between the communicating parties. Steganography experiments on active and inactive voice periods were carried out using a VoIP communications system. Performance evaluation and security analysis indicates that the proposed VoIP steganographic scheme can withstand statistical detection, and achieve secure real-time covert communications with high speech quality and negligible signal distortion.

Telecare medicine information systems (TMIS) provide flexible and convenient e-health care. However, the medical records transmitted in TMIS are exposed to unsecured public networks, so TMIS are more vulnerable to various types of security threats and attacks. To provide privacy protection for TMIS, a secure and efficient authenticated key agreement scheme is urgently needed to protect the sensitive medical data. Recently, Mishra et al. proposed a biometrics-based authenticated key agreement scheme for TMIS by using hash function and nonce, they claimed that their scheme could eliminate the security weaknesses of Yan et al.'s scheme and provide dynamic identity protection and user anonymity. In this paper, however, we demonstrate that Mishra et al.'s scheme suffers from replay attacks, man-in-the-middle attacks and fails to provide perfect forward secrecy. To overcome the weaknesses of Mishra et al.'s scheme, we then propose a three-factor authenticated key agreement scheme to enable the patient to enjoy the remote healthcare services via TMIS with privacy protection. The chaotic map-based cryptography is employed in the proposed scheme to achieve a delicate balance of security and performance. Security analysis demonstrates that the proposed scheme resists various attacks and provides several attractive security properties. Performance evaluation shows that the proposed scheme increases efficiency in comparison with other related schemes.

Low bit-rate speech codec offers so many advantages over other codecs that it has become increasingly popular in audio communications such as mobile and Voice over Internet Protocol (VoIP) communications, and thus, researching steganography in low bit-rate speech codec is of important significance. In this study, we proposed a universal VoIP steganography model for low bit-rate speech codec that uses the PESQ deterioration rate and the decoding error to automatically choose a data-embedding algorithm for each VoIP bitstream, which enables ones to achieve covert communications using a low bit-rate speech codec efficiently and securely. Because no or little attention has been paid to steganography in Internet Speech Audio Codec, it was chosen as the test codec to verify the effectiveness, security, and practicability of the proposed steganography model. The experimental results show that, with the proposed steganography model, it achieved the average PESQ deterioration rate of 4.04% (less than 5%, indicating strong imperceptibility) and a high data-hiding capacity up to 12 bits/frame (400 bits/s, three times larger than other methods), and the proposed steganography model could effectively resist the latest steganalysis. Copyright © 2015 John Wiley & Sons, Ltd.

The last few years have witnessed an explosive growth in the research of information hiding in multimedia objects, but few studies have taken into account packet loss in multimedia networks. As one of the most popular real-time services in the Internet, Voice over Internet Protocol (VoIP) contributes to a large part of network traffic for its advantages of real time, high flow, and low cost. So packet loss is inevitable in multimedia networks and affects the performance of VoIP communications. In this study, a fractal-based VoIP steganographic approach was proposed to realize covert VoIP communications in the presence of packet loss. In the proposed scheme, secret data to be hidden were divided into blocks after being encrypted with the block cipher, and each block of the secret data was then embedded into VoIP streaming packets. The VoIP packets went through a packet-loss system based on Gilbert model which simulates a real network situation. And a prediction model based on fractal interpolation was built to decide whether a VoIP packet was suitable for data hiding. The experimental results indicated that the speech quality degradation increased with the escalating packet-loss level. The average variance of speech quality metrics (PESQ score) between the “no-embedding” speech samples and the “with-embedding” stego-speech samples was about 0.717, and the variances narrowed with the increasing packet-loss level. Both the average PESQ scores and the SNR values of stego-speech samples and the data-retrieving rates had almost the same varying trends when the packet-loss level increased, indicating that the success rate of the fractal prediction model played an important role in the performance of covert VoIP communications.

A suitable key agreement protocol plays an essential role in protecting the communications over open channels among users using voice over Internet protocol (VoIP). This study presents a robust and flexible password authenticated key agreement protocol with user anonymity for session initiation protocol (SIP) used by VoIP communications. Security analysis demonstrates that the proposed protocol enjoys many unique properties, such as user anonymity, no password table, session key agreement, mutual authentication, password updating freely, conveniently revoking lost smartcards and so on. Furthermore, the proposed protocol can resist the replay attack, the impersonation attack, the stolen‐verifier attack, the man‐in‐middle attack, the Denning‐Sacco attack and the offline dictionary attack with or without smartcards. Finally, the performance analysis shows that the protocol is more suitable for practical application in comparison with other related protocols.

ABSTRACT Session Initiation Protocol (SIP) is one of the most commonly used protocols for handling sessions for over Internet Protocol based communications, and the security of SIP is becoming increasingly important. Recently, Zhang et al. proposed a password‐authenticated key agreement protocol for SIP by using smart cards to protect the VoIP communications between users. Their protocol provided some unique features, such as mutual authentication, no password table needed, and password updating freely. In this study, we performed cryptanalysis of Zhang et al.'s protocol and found that their protocol was vulnerable to the impersonation attack although the protocol could withstand several other attacks. A malicious attacker could compute other users' privacy keys and then impersonated the users to cheat the SIP server. Furthermore, we proposed an improved password‐authentication key agreement protocol for SIP, which overcame the weakness of Zhang et al.'s protocol and was more suitable for Voice over Internet Protocol communications. Copyright © 2014 John Wiley & Sons, Ltd.

Service composition-based network software customisation is currently a research hotspot in the field of software engineering. A key problem of the hotspot is how to efficiently discover services distributed over the Internet. In the service oriented architecture, service discovery suffers from the performance bottleneck of centralised universal description discovery and integration (UDDI), and inaccurate matching of service semantics. In this study, the authors describe a novel method for service labelling, registration and discovery, which is based on the role-goal-process-service meta-model. This approach enables ones to achieve accurate matching of service semantics by extending web service description language with RGP demand-information. The authors also suggest a peer-to-peer (P2P)-based architecture of service discovery to address the issues in the UDDI bottleneck and the complexity of semantic computation. By adopting the proposed approach, an experiment prototype system has been designed and implemented in Beijing municipal transportation system. The experimental results show the proposed approach is effective in addressing the aforementioned problems.

Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits/frame (133.3 bits/second).

A network covert channel is a passage along which information leaks across the network in violation of security policy in a completely undetectable manner. This study reveals our findings in analysing the principle of G.723.1 codec that there are ‘unused’ bits in G.723.1 encoded audio frames, which can be used to embed secret messages. A novel steganalysis method that employs the second detection and regression analysis is suggested in this study. The proposed method can not only detect the hidden message embedded in a compressed voice over Internet protocol (VoIP) speech, but also accurately estimate the embedded message length. The method is based on the second statistics, that is, doing a second steganography (embedding information in a sampled speech at an embedding rate followed by embedding another information at a different level of data embedding) in order to estimate the hidden message length. Experimental results have proven the effectiveness of the steganalysis method for detecting the covert channel in the compressed VoIP speech.

This paper describes a novel high-capacity steganography algorithm for embedding data in the inactive frames of low bit rate audio streams encoded by G.723.1 source codec, which is used extensively in Voice over Internet Protocol (VoIP). This study reveals that, contrary to existing thought, the inactive frames of VoIP streams are more suitable for data embedding than the active frames of the streams; that is, steganography in the inactive audio frames attains a larger data embedding capacity than that in the active audio frames under the same imperceptibility. By analyzing the concealment of steganography in the inactive frames of low bit rate audio streams encoded by G.723.1 codec with 6.3 kb/s, the authors propose a new algorithm for steganography in different speech parameters of the inactive frames. Performance evaluation shows embedding data in various speech parameters led to different levels of concealment. An improved voice activity detection algorithm is suggested for detecting inactive audio frames taking into packet loss account. Experimental results show our proposed steganography algorithm not only achieved perfect imperceptibility but also gained a high data embedding rate up to 101 bits/frame, indicating that the data embedding capacity of the proposed algorithm is very much larger than those of previously suggested algorithms.

In this paper we introduce a novel codebook partition algorithm for quantization index modulation (QIM), which is applied to information hiding in instant low bit-rate speech stream. The QIM method divides the codebook into two parts, each representing '0' and '1' respectively. Instead of randomly partitioning the codebook, the relationship between codewords is considered. The proposed algorithm - complementary neighbor vertices (CNV) guarantees that every codeword is in the opposite part to its nearest neighbor, and the distortion is limited by a bound. The feasibility of CNV is proved with graph theory. Moreover, in our work the secret message is embedded in the field of vector quantization index of LPC coefficients, getting the benefit that the distortion due to QIM is lightened adaptively by the rest of the encoding procedure. Experiments on iLBC and G.723.1 verify the effectiveness of the proposed method. Both objective and subjective assessments show the proposed method only slightly decreases the speech quality to an indistinguishable degree. The hiding capacity is no less than 100 bps. To the best of our knowledge, this is the first work adopting graph theory to improve the codebook partition while using QIM in low bit-rate streaming media.