Existing speech steganalysis suffers from generalization in dynamic scenarios such as unknown or random Embedding Strengths (EBS). The central challenge is domain mismatch, a problem that remains largely unexplored in speech steganalysis. To fill the gap, this letter proposes a novel multi-source domain adaptation method called Fuzzy Clustering-Based Domain Adaptation (FCDA). First, to enable effective clustering of samples with similar actual EBS, FCDA employs Fuzzy C-Means (FCM) clustering, allowing soft estimation across multiple Embedding Rate (EBR) levels. Second, to enhance the steganographic classification performance, we construct the backbone network integrating a dual-primary classifier with an auxiliary EBR hierarchy classifier, which leverages the strength-sensitive representation. Third, to better realize the training of multi-objective tasks and enhance feature discriminability, the domain-consistent optimization loss is introduced. Experimental results show that FCDA outperforms state-of-the-art steganalysis methods and multi-source domain adaptation methods.

Universal low bit-rate speech steganalysis is a cutting-edge research task addressing real-world application needs and has garnered significant attention recently. However, the existing methods are still inadequate in extracting available information from various steganographic domains and fail to deliver interpretable forensic results for specific steganographic domains containing embedded information. In view of this, we present a novel universal low bit-rate speech steganalysis approach that seamlessly combines domain-specific and domain-shared information, enabling comprehensive and effective speech steganography detection. This approach comprises two vital components: the Matching Identification Network (MIN) and the Content Alignment Network (CAN). The MIN incorporates three effective separable backbones for capturing informative domain-specific embeddings, inherently unveiling local and global dependencies. In this network, we also design a cross-domain matching module to establish correlations among steganographic domains, thereby enhancing detection performance through multi-domain collaboration and facilitating effective forensics for the embedded domains. Moreover, the CAN acquires more informative domain-shared embeddings by using a metric learning-based Siamese architecture to process pairs of naive and recompressed speech samples. Experimental results demonstrate that the presented method not only significantly surpasses the existing universal steganalysis methods, but also competes with or even surpasses dedicated steganalysis methods in certain cases. In addition, our method can provide accurate forensic results regarding the existence of hidden information within each steganographic domain without relevant supervisory information, marking a significant milestone in pursuit of speech steganalysis. The source code for this work will be publicly available on GitHub.

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audiowatermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%. Our audio samples are available at https://groot-gaw.github.io/.

Conventional time-delay neural networks (TDNNs) struggle to handle long-range context, their ability to represent speaker information is therefore limited for long utterances. Existing solutions either depend on increasing model complexity or try to strike a balance between local features and global context to address this issue. To effectively leverage the long-term dependencies of audio signals and constrain model complexity, we introduce a novel module called Global-aware Filter layer (GF layer) in this work, which employs a set of learnable transform-domain filters between a 1D discrete Fourier transform and its inverse transform to capture global context. Additionally, we develop a dynamic filtering strategy and a sparse regularization method to enhance the performance of the GF layer and prevent overfitting. Based on the GF layer, we present a dual-stream TDNN architecture called DS-TDNN for automatic speaker verification (ASV), which utilizes two unique branches to extract both local and global features in parallel and employs an efficient strategy to fuse different-scale information. Experiments on the Voxceleb and SITW databases demonstrate that the DS-TDNN achieves a relative improvement of 10% together with a relative decline of 20% in computational cost over the ECAPA-TDNN in the speaker verification task. This improvement becomes more evident as the utterance's duration grows. Furthermore, the DS-TDNN also beats popular deep residual models and attention-based systems on utterances of arbitrary length.

The ability to detect multiple steganographic methods in speech streams is an important prerequisite for steganalysis methods to move from theory to practical application, but it is also a challenging problem. To address this challenge, we propose a novel steganalysis method based on multi-agent deep learning, which can effectively detect multiple steganography methods in speech streams. Our method utilizes multiple agents to learn the features of multiple sub-training datasets separately and then fuses the information of each agent through the weight parameter aggregation mechanism to obtain the final weight parameter of the steganalysis model. Experimental results show that our proposed method outperforms the state-of-art steganalysis methods. In particular, for low embedding rates, the presented method increases average detection accuracy by about 9%.

In the context of healthcare 4.0, cloud‐based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper‐proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy‐preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state‐of‐the‐art ones.

The real-time detection of speech steganography in Voice-over-Internet-Protocol (VoIP) scenarios remains an open problem, as it requires steganalysis methods to perform for low-intensity embeddings and short-sample inputs, as well as provide rapid detection results. To address these challenges, this paper presents a novel steganalysis model based on spatial and temporal feature fusion (STFF-SM). Differing from the existing methods, we take both the integer and fractional pitch delays as input, and design subframe-stitch module to organically integrate subframe-wise integer delays and frame-wise fractional pitch delays. Further, we design a spatial fusion module based on pre-activation residual convolution to extract the pitch spatial features and gradually increase their dimensions to discover finer steganographic distortions to enhance the detection effect, where a Group-Squeeze-Weighting block is introduced to alleviate the information loss in the process of increasing the feature dimension. In addition, we design a temporal fusion module to extract pitch temporal features using the stacked LSTM, where a Gated Feed-Forward Network is introduced to learn the interaction between different feature maps while suppressing the features that are not useful for detection. We evaluated the performance of STFF-SM through comprehensive experiments and comparisons with the state-of-the-art solutions. The experimental results demonstrate that STFF-SM can well meet the needs of real-time detection of speech steganography in VoIP streams, and outperforms the existing methods in detection performance, especially with low embedding strengths and short window sizes.

Steganography based on fixed codebook has become one of the most important branches of speech steganography due to its high imperceptibility and having the largest available carrier space. As its countermeasure technique, this paper presents a novel steganalysis method based on separable convolution network (SepSteNet) with dual-stream pyramid enhanced strategy (DPES). Specifically, to better acquire discriminative representations, we design the pulse-aware separable block to capture the pulse correspondence along independent levels of pulse positions, where the pulse-aware excitation module is plugged to avoid noisy clue accumulation by adaptively emphasizing the salient part. Moreover, the global attending block is introduced to enhance correspondence features through calculating global responses at distinct subframes. In addition, to eliminate the negative impact of sample content, DPES is leveraged to incorporate cross-domain coherence features by the inverted connected dual-stream branches. With the original and calibration speech samples, two branches enable the correspondence of two detection feature domains to interact with each other to generate coherence features independent of sample content, thereby improving the detection performance. The performance of the presented method is comprehensively evaluated and compared with the state of the arts. The experimental results demonstrate that the presented method significantly outperforms the existing ones. Furthermore, DPES is shown to be a general enhancement strategy that can effectively improve the performance of the existing deep neural network for speech steganalysis. The source code for this work will be publicly available on GitHub.

To prevent the abuse of low-rate speech-based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low-bit-rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self-paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under-sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under-sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low-bit-rate speech-based steganography, and the steganalysis scheme based on the self-paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self-paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self-paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.

With the development of speech steganography technology, steganographers are more and more inclined to realize more secure covert communication by combining a series of steganography methods. Thus, this letter presents a novel multi-encoder network (MENet) to achieve more efficient detection of multiple steganography methods. Differing from the previous work, MENet utilizes multiple private encoders to individually model the private features of each coding element, introduces a shared encoder based on an attention mechanism to fuse multiple private features for achieving better feature representation, and finally exploits a shared decoder to reduce feature dimensionality as well as give predictions. Taking the existing state-of-the-art steganography methods as the detection targets, the performance of the proposed steganalysis method is evaluated comprehensively and compared with the state-of-the-art ones. The experimental results show that the detection performance of MENet is overall better than the existing steganalysis methods, especially with low embedding rates and short speech sample lengths.

The Internet of Vehicles (IoV) , with the help of cloud computing, can provide rich and powerful application services for vehicles and drivers by sharing and analysing various IoV data. However, how to ensure the integrity of IoV data with multiple sources and diversity outsourced in the cloud is still an open challenge. To address this concern, this paper first presents an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing and privacy protection. Particularly, we design a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. Moreover, our scheme can reduce the overheads for both the key management and the generation of block tags. We formally prove the security of the presented scheme and evaluate its performance by comprehensive comparisons with the state-of-the-art schemes designed for traditional scenarios. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data, and outperforms the previous ones in both the computation and communication overheads in most cases.

This paper proposes a new framework of lossless data hiding (LDH) in JPEG images. The proposed framework contains two algorithms, i.e., the optimized basic LDH and the relay transfer based extension. In the basic algorithm, we aim to preserve the filesize after data embedding. The data hiding process is optimized by variable-length-code (VLC) mapping, combination and permutation. In the extended algorithm, we focus on embedding more bits into the bitstream with a condition that the filesize increment is allowed. To decrease the filesize increment, we propose a relay transfer based algorithm to preprocess the JPEG bitstream. Subsequently, we embed data into the processed bitstream using the basic LDH. Both algorithms provide better performances than previous arts. After lossless data hiding, the marked JPEG bitstream is compliant to common JPEG decoders. Since all operations are implemented on VLCs and the Huffman codes, no distortion is generated on the image pixels. Experimental results demonstrate that the proposed approach outperforms previous methods.

Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high‐speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency‐hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state‐of‐the‐art ones in computing overheads for updating and verification.

Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing techniques to strengthen data owners' trust and confidence in cloud storage. In this paper, we present a novel public auditing scheme for secure cloud storage based on dynamic hash table (DHT), which is a new two-dimensional data structure located at a third parity auditor (TPA) to record the data property information for dynamic auditing. Differing from the existing works, the proposed scheme migrates the authorized information from the CSP to the TPA, and thereby significantly reduces the computational cost and communication overhead. Meanwhile, exploiting the structural advantages of the DHT, our scheme can also achieve higher updating efficiency than the state-of-the-art schemes. In addition, we extend our scheme to support privacy preservation by combining the homomorphic authenticator based on the public key with the random masking generated by the TPA, and achieve batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme, and evaluate the auditing performance by detailed experiments and comparisons with the existing ones. The results demonstrate that the proposed scheme can effectively achieve secure auditing for cloud storage, and outperforms the previous schemes in computation complexity, storage costs and communication overhead.

As the production, storage, and exchange of information become more extensive and important in the functioning of societies, the problem of protecting the information from unintended and undesired usage becomes more complex. In modern societies, protection of information involves many interdependent technological and policy issues related to information confidentiality, integrity, anonymity, authenticity, utility, etc. Information hiding techniques are receiving much attention today. Digital audio, video, and images are increasingly furnished with distinguishing but imperceptible marks, which may contain a hidden copyright notice or serial number or even help to prevent unauthorized copying directly. Digital watermarking and steganography may protect information, conceal secrets, or are used as core primitives in digital rights' management schemes. Alongside the previously mentioned types of digital media steganography, currently, the target of increased interest is network steganography—a part of information hiding focused on modern networks. It is a method of hiding secret data in users' normal data transmissions. Steganographic techniques arise and evolve with the development of network protocols and mechanisms and are expected to be used in secret communication or information sharing. Presently, it becomes a hot topic because of the proliferation of information networks and multimedia services in networks and social networks. The purpose of establishing applications of Information Hiding may be varied—possible uses can fall into the category of legal actions or illicit activity. Frequently, the illegal aspect is accentuated—starting from the criminal communication, through information leakage from protected systems, cyber weapon exchange, up to industrial espionage. Recently discovered malware like Hammertoss or Stegoloader utilize various information hiding techniques for botnets purposes to enable covert communication for the C and C (Command and Control) channel. This makes detection of such malware even more difficult, and it poses a serious challenge also to investigators. On the other side of the spectrum lies legitimate uses, which include circumvention of web censorship and surveillance, computer forensics (tracing and identification), and copyright protection (e.g., watermarking images). In this special issue, we are delighted to present a selection of nine papers, which, in our opinion, will contribute to the enhancement of knowledge in information hiding. The collection of high-quality research papers provides a view on the latest research advances on covert communication, steganography, and steganalysis. In the first paper, Multi-bit watermarking of high dynamic range images based on perceptual models, Emanuele Maiorana and Patrizio Campisi describe a multi-bit watermarking method dedicated to high dynamic range images. The proposed method takes advantage of various perceptual features of the human eye. The authors present the results of imperceptibility and robustness tests, using a database of 15 high dynamic range images. Also two next articles concern image processing. In MDE-based image steganography with large embedding capacity, Zhaoxia Yin and Bin Luo propose a steganographic method based on modification of direction exploitation and pixel pair matching. The algorithm is explained in detail, and a numerical example is given. The authors analyze also the quality of the conveyed covert image and evaluate security of the proposed algorithm, using two steganalysis methods. Fengyong Li, Xinpeng Zhang, Hang Cheng, and Jiang Yu in Digital image steganalysis based on local textural features and double dimensionality reduction also deal with steganalysis of image steganography. They propose a spatial steganalysis scheme based on local textural features and double dimensionality reduction. The authors demonstrate effectiveness of their method using 5000 greyscale images and three different steganographic techniques. Next three articles concern using audio signals for steganographic transmission. In the first of them, Real-time audio steganography attack based on automatic objective quality feedback, Qilin Qi, Aaron Sharp, Dongming Peng, and Hamid Sharif propose an active warden steganographic attack based on discrete spring transform with the use of an objective quality assessment. The authors show effectiveness of such an attack against two different steganographic techniques—spread spectrum-based and a time-scale modification-robust steganography. Shanyu Tang, Qing Chen, Wei Zhang, and Yongfeng Huang in Universal steganography model for low bit-rate speech codec describe a universal steganography model for low bit-rate speech codec. The proposed method is based on using perceptual evaluation of speech quality algorithm to choose a proper data hiding algorithm. The authors employ proposed approach for the Internet Speech Audio Codec and present results for the steganographic bandwidth and cost. Rennie Archibald and Dipak Ghosal in Design and performance evaluation of a covert timing channel discuss covert timing channels, in which the steganographic transmission is realized by modulating the inter-packet delay times. The authors propose a method, which minimizes overruns and underruns of an Internet Protocol phone buffer, and then evaluate its performance using Skype traffic. Pawel Laka and Lukasz Maksymiuk in Steganographic transmission in optical networks with the use of direct spread spectrum technique describe their method of steganographic transmission to be used in the physical layer of optical networks. The proposed method is based on the spread spectrum technique. The authors show results of their experiments with adjusting the spreading code length and optical power level dependencies. In the next article, On importance of steganographic cost for network steganography, an analysis of steganographic cost for network steganography is presented by Wojciech Mazurczyk, Steffen Wendzel, Ignacio Azagra Villares, and Krzysztof Szczypiorski. In this paper, the metric of steganographic cost is defined as degradation or a distortion of the carrier caused by the application of the steganographic method. The authors analyze various approaches to steganographic cost in selected single-method and multi-method steganographic techniques. In the last of the presented articles, Matrix embedding in multicast steganography: analysis in privacy, security and immediacy, Weiwei Liu, Guangjie Liu, and Yuewei Dai discuss multicast steganography, in which a single sender delivers simultaneously different secret messages to several receivers within the same cover object. The authors propose both synchronous and asynchronous multicast matrix embedding frameworks, based on Slepian–Wolf coding and overlapped multi-embedding, respectively. Privacy, security, and immediacy of the proposed solutions are also discussed. To summarize, we believe that this Special Issue will contribute to enhancing knowledge in Information and Communication Technology (ICT) security and in information hiding in particular. In addition, we also hope that the presented results will stimulate further research in the important areas of information and network security, including steganography and covert communication. We also want to thank the editor-in-chief of the Security and Communication Networks journal, the leading researchers contributing to the special issue and excellent reviewers for their great help and support that made this special issue possible.

Abstract Steganography in low bit‐rate speech streams is an important branch of Voice‐over‐Internet Protocol steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support‐vector‐machine‐based steganalysis of low bit‐rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long‐time distribution feature, extract Markov transition probabilities of pulse positions according to the short‐time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse‐to‐pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a‐encoded speech samples and compare it with the state‐of‐the‐art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy, false positive rate, and false negative rate at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods. Copyright © 2016 John Wiley & Sons, Ltd.

Steganography in low bit-rare speech streams is an important branch of Voice-over-IP steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support-vector-machine based steganalysis of low bit-rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long-time distribution feature, extract Markov transition probabilities of pulse positions according to the short-time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse-to-pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a encoded samples, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods.

Tracking mobile targets is one of the most important applications in wireless sensor networks (WSNs). Traditional tracking solutions are based on fixed sensor nodes and have two critical problems. First, in WSNs, the energy constraint is a main concern, but due to the mobility of targets, lots of sensor nodes in WSNs have to switch between active and sleep states frequently, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected while moving in the holes. To solve these problems, this paper exploits a few of mobile sensor nodes to continuously track mobile targets because the energy capacity of mobile nodes is less constrained. Based on a realistic detection model, a solution for scheduling mobile nodes to cooperate with ordinary fixed nodes is proposed. When targets move, mobile nodes move along with them for tracking. The results of extensive simulations show that mobile nodes help to track the target when holes appears in the coverage area and extend the effective monitoring time. Moreover, the proposed solution can effectively reduce the energy consumption of sensor nodes and prolong the lifetime of the networks.

As the production, storage, and exchange of information become more extensive and important in the functioning of societies, the problem of protecting the information from unintended and undesired usage becomes more complex. In modern societies, protection of information involves many interdependent technological and policy issues related to information confidentiality, integrity, anonymity, authenticity, utility, and so on. Information hiding techniques are receiving much attention today. Digital audio, video, and images are increasingly furnished with distinguishing but imperceptible marks, which may contain a hidden copyright notice or serial number or even help to prevent unauthorized copying directly. Digital watermarking and steganography may protect information, conceal secrets, or are used as core primitives in digital rights' management schemes. Alongside the aforementioned types of digital media steganography, currently, the target of increased interest is network steganography—a part of information hiding focused on modern networks. It is a method of hiding secret data in users' normal data transmissions. Steganographic techniques arise and evolve with the development of network protocols and mechanisms and are expected to be used in secret communication or information sharing. Presently, it becomes a hot topic owing to the proliferation of information networks and multimedia services in networks and social networks. The purpose of establishing applications of information hiding may be varied—possible uses can fall into the category of legal actions or illicit activity. Frequently, the illegal aspect is accentuated—starting from criminal communication, through information leakage from protected systems and cyber weapon exchange, up to industrial espionage. Recently discovered malware such as Duqu and Alureon point to the alleged utilization of information hiding techniques in botnets for covert communication in the command-and-conquer channels, which proves that information hiding poses important challenges to investigators. On the other side of the spectrum lie legitimate uses, which include circumvention of web censorship and surveillance, computer forensics (tracing and identification), and copyright protection (e.g. watermarking images). Papers must be written in English and describe original research not published or currently under review by other journals or conferences. All relevant papers submitted will go through an external review process. Concerning the preparation of the manuscript, please refer to the “Instructions for Authors” page at the journal website, http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291939-0122/homepage/ForAuthors.html Furthermore, the manuscript must be submitted through the online submission system: http://mc.manuscriptcentral.com/scn by selecting the corresponding track. When submitting papers, the authors should identify “Manuscript Type” as “Special Issue,” enter “Running Head” as “SCN-SI-066” and “Special Issue Title” as “Trends in modern information hiding: techniques, applications and detection.” Contributing authors might also be asked to review some of the papers submitted to this special issue. All papers will be rigorously reviewed on the basis of their quality: originality, high scientific quality, good organization and clear writing, sufficient support for assertions and conclusion, appropriate title, abstract that include important points of the paper, satisfactory English, pertinent references, and clear tables and figures. Manuscript submission: 1 March 2014 Acceptance/rejection notification: before 1 July 2014 Expected publication: 2015

For steganography over speech frames, least-significant-bit (LSB) approach has been one of the most important alternatives. However, its embedded capacity is quite limited, due to the low redundancy characteristic of low bitrate speech. Thus, we suggest a novel least significant digit (LSD) method in this paper, which makes full use of the frame bits to hide secret messages and provides a larger embedding capacity than the LSB method. The LSD method exploits the multiple adjacent states of frame parameters, which are produced by multiple modifications (e.g. +1, -1, +2, -2) and encoded as LSDs using a multi-ary numeration system. Beyond providing a considerable embedding capacity, the LSD method further extends the key space for key-based steganography, which can enhance the security of covert communication. The LSD method is implemented and evaluated with G.723.1 as the codec for speeches. The experiment results show that, compared with the previous LSB method, the LSD method increases around 30% of embedding capacity, and induces less distortion given the same embedding capacity.

Common least significant bit (LSB) relevant steganography methods in speech frames often base on bits evaluation by certain speech quality evaluation criterion and together with some coding and embedding strategies to enhance imperceptibility and efficiency. However, some embedding capabilities and security strategy are neglected. This paper proposes a state-based steganography method which fully investigates speech frame features in order to expand embedding capabilities and enhance steganography security. In the proposed method, embedding capabilities are measured by available numbers of states relative to current frame parameters rather than information bits. And secret information embedding procedure is performed by the chosen states mapped operations. This is a basic fine-grained steganography solution which is useful for other algorithms based on it. The experimental results have demonstrated that statebased method outperforms traditional LSB substitution in overall performance and introduces limited latency, which meets the realtime requirement in covert communications.

Embedding transparency is one of the most important criteria for steganography. This paper mainly focuses on transparency-orientated encoding strategies for steganography on Voice over IP (VoIP). Our analysis of the existing encoding strategies proposed for steganography on storage media reveals that they have limited applicability in VoIP-based steganography because they enhance embedding transparency at the expense of a decreased embedding rate (ER). In this paper, we propose three encoding strategies based on digital logic for steganography on VoIP. Differing from the existing approaches, our strategies reduce the embedding distortion by improving the similarity between the cover and the covert message using digital logical transformations, instead of reducing the ER. Therefore, in contrast, our strategies improve the embedding transparency without sacrificing the embedding capacity. Of these three strategies, one adopts logical operations, one employs circular shifting operations and the third combines the operations of the first two. All these schemes are evaluated experimentally through their prototype implementations that are compared with some existing methods in a prototypical covert communication system based on VoIP (called StegVoIP). The experimental results show that the proposed strategies can provide better embedding performance than the existing approaches in terms of embedding transparency and ER.

Differing from applying steganography on storage cover media, steganography on voice over IP (VoIP) must often delicately balance between providing adequate security and maintaining low latency for real-time services. This paper presents a novel real-time steganography model for VoIP that aims at providing good security for secret messages without sacrificing real-time performance. We achieve this goal by employing the well-known least-significant-bits (LSB) substitution approach to provide a reasonable tradeoff between the adequate information hiding requirement (good security and sufficient capacity) and the low latency requirement for VoIP. Further, we incorporate the M-sequence technique to eliminate the correlation among secret messages to resist the statistical detection based on the fact that the distribution of the LSBs in the stego-speech is not uniform and to provide a short-term security protection of secret messages. To accurately recover secret messages at the receiver side, we design a synchronization mechanism based on the RSA key agreement and the synchronized sequence transmission using techniques of the protocol steganography, which can effectively enhance the flexibility of the covert communication system and be extended to other steganography schemes based on real-time systems. We evaluate the effectiveness of our model with ITU-T G.729a as the codec of the cover speech in StegTalk, a covert communication system based on VoIP. The experimental results demonstrate that our technique provides good security and transparency for transmitting secret messages while adequately meeting the real-time requirement of VoIP.

This paper presents an adaptive steganography scheme for Voice over IP (VoIP). Differing from existing steganography techniques for VoIP, this scheme enhances the embedding transparency by taking into account the similarity between least significant bits (LSBs) and embedded messages. Moreover, we introduce the notion of partial similarity value (PSV). By properly setting the threshold PSV, we can adaptively balance the embedding transparency and capacity. We evaluate the effectiveness of this scheme with G.729a as the codec of the cover speech in StegTalk, a covert communication system based on VoIP. The experimental results demonstrate that our technique provides better performance than the traditional method.

This paper presents three encoding strategies based on digital logic for steganography on Voice over IP (VoIP), which aim to enhance the embedding transparency. Differing from previous approaches, our strategies reduce the embedding distortion by improving the similarity between the cover and the covert message using digital logical transformations, instead of reducing the amount of the substitution bits. Therefore, by contrast, our strategies will improve the embedding transparency without sacrificing the embedding capacity. Of these three strategies, the first one adopts logical operations, the second one employs circular shifting operations, and the third one combines the operations of the first two. All of them are evaluated through comparing their prototype implementations with some existing methods in a prototypical covert communication system based on VoIP (called StegVoIP). The experimental results show that the proposed strategies can effectively enhance the embedding transparency while maintaining the maximum embedding capacity.