Adaptive VoIP Steganography for Information Hiding within Network Audio Streams

2011 International Conference on Network-Based Information Systems Adaptive VoIP Steganography for Information Hiding within Network Audio Streams Erchi Xu, Bo Liu, Liyang Xu, Ziling Wei, Baokang Zhao,JinshuSu School of Computer Science National University of Defense Technology P.R. of China {bkzhao,sjs}@nudt.edu.cn Abstract With the rapid development of the Internet, steganography on Voice over IP (VoIP) has been attracted a lot of research efforts. To date, existing VoIP steganography research commonly focus on information hiding in the LSB bits of Network Audio Streams, yet, we found this approach may raise serious security threat, where the hidden information may be easily removed, detected and attacked. Towards this issue, in this paper, we propose AVIS, a novel Adaptive VoIP steganography approach to hide information within Network Audio Streams. AVIS consists of two parts, named VAMI and VADDI. VAMI works by dynamically select multiple bits based on the VoIP vector value, while VADDI dynamically changes embedding intervals to avoid detection and attacking. We also implemented AVIS and conducted extensive experiments in real systems. Experimental results demonstrate the effectiveness of our proposed AVIS scheme. I. INTRODUCTION Differs from the cryptography techniques that only conceals the content of the message, the steganography focus on hiding the presence of the message itself by providing important approaches to protect the information transmission channels. Therefore, the steganography issues have attracted a lot of research efforts[1]. In the earlier years, the steganography techniques are focused on static file based information hidding, such as images, text files[2], [3]; yet, with the rapid deployment of the Internet, network-based steganography has becoming more and more critical and challenging[4], especially for information hidden in VoIP audio streams. So far, many classic steganography technologies tend to hide the information within the LSB(Least Significant Bits) [5], [6], [7], [8] of the stream, which makes it possible for detecting and attacking methods to interfere the information transmission. However, detecting and attacking measures such as NVR (neighbor vectors ratio) detection [9], LSB interference can still be effective by adjusting to the VoIP stream platform. To enhance the anti-detecting and anti-attacking ability of the VoIP steganography, in this paper, we propose AVIS(Adaptive VoIP Steganography), a novel approach to enhance the VoIP steganograhy by setting embedding places and embedding intervals dynamically. AVIS consists of two parts, VAMI(VAlue-based Multiple Insertion) and VADDI(Voice Activity Detection Dynamic Insertion). In VAMI, instead of simply hiding in LSB only, we dynamically select multiple bits based on the VoIP vector value. Consequently, not only LSB interference and NVR detection will no longer be effective but also the quality of transmission will be better than simply hiding information in relatively high bits. Although VAMI method can cheat the NVR detection, the NVR detection can still be effective by not only detecting the LSB neighbor vectors but also the other higher bits. Aiming at overcoming potential security threats, in AVIS strategy, we propose the other method VADDI to dynamically change embedding intervals.by applying VADDI, the information will only be embedded when people talking in the VoIP communication. By this mean, the VoIP stream will not satisfy the NVR detection conditions and, due to HAS (Human Audio System) covering effects, it is rarely possible for adversaries to eavesdropping the audio streams. The remainder part of this paper is organized as follows. The preliminaries are reviewed in section II. We propose our AVIS scheme in section III. After extensive experiments, the results and detailed analysis are described in Section IV. II. PRELIMINARIES A. the VoIP information Embedding Process Figure 1. The Sender Process 978-0-7695-4458-8/11 $26.00 2011 IEEE DOI 10.1109/NBiS.2011.103 612

In the VoIP information embedding process, as illustrated in Fig 1, senders can allocate information in the VoIP communication speech packet. The speech packet is denoted by C. The information space is denoted as HS and will be embedded with information. Let us denote the secret information as S. Suppose we obtain S by dividing S, and hide S into HS. The modified speech packet (which contains S ) is called the steganographified speech, which is denoted by G. Now G can be sent to the receiver via public Internet. The receiver may reverse the process to get the information S. B. The Classic LSB steganography According to the stego process, we know that stego needs HS and embedding interval to nail down the embedding place. For example, in [4], in order to arouse less sound distortion, the passage chooses to embed information in LSB and choose half of the speech packet to hide information. As shown in the Fig 2, in speech packets all odd speech vectors form 1 to (N-1) are embedded with information. By embedding in LSB, the process will only arouse a litter sound distortion and relative broad bandwidth. Figure 2. The typical message embedding process in LSB steganography C. the audio quality In general, the Mean Opinion Score (MOS) provides a numerical indication of the perceived quality of received media after compression or transmission. The MOS value is expressed from 1 to 5, where 1 stands for the lowest quality and 5 stands for highest voice quality. According to ITU-T[10], more than 16 experts are required to obtain MOS. Yet, due to drawbacks including high expense and time-consumption of obtaining MOS, ITU came up with PESQ[10] to simulate a listening test and is optimized to reproduce the average result of all listeners. Thus users can use PESQ algorithm to estimate the codec quality quickly. Table I THE MOS STANDARD Mean opinion score (MOS) MOS Quality Impairment 5 Excellent Imperceptible 4 Good Perceptible but not annoying 3 Fair Slightly annoying 2 Poor Annoying 1 Bad Very annoying Table II THE G.711 CODEC Mode Linear input vector(l) Compressed vector(c) Bit-weight 0 s00000001wxyza... s000wxyz 2 3 1 s0000001wxyzab... s001wxyz 2 4 2 s000001wxyzabc... s010wxyz 2 5 3 s00001wxyzabcd... s011wxyz 2 6 4 s0001wxyzabcde... s100wxyz 2 7 5 s001wxyzabcdef... s101wxyz 2 8 6 s01wxyzabcdefg... s110wxyz 2 9 7 s1wxyzabcdefgh... s111wxyz 2 10 III. THE PROPOSED AVIS SCHEME Classic steganography technologies usually choose LSB for its low distortion. However, using LSB actually has potential threats due its vulnerability to attacking and detecting. Therefore, we proposed AVIS. In this section, we will describe the VAMI and VADDI sequentially. A. VAMI Since VAMI is dedicated to vary the embedding place for information hiding, it is better to utilize the potential embedding bits as much as possible. So we firstly should judge whether the potential embedding bit is available for each mode. 1) G.711-PCMU Codec analysis: Let s take the G.711- PCMU codec as a VAMI example. As shown in table II [6], G.711 converts a 16- bits linear input vector L to a compressed vector C as, The notation bits of L are same as C. Following the notation bit, the next three bits of C, also known as the Mode bits, are used to mark the place of first 1 shown in L. The w, x, y and z bit of L remain the same in C. As shown in table. II, there are two properties of G.711 codec as follows, The value of C is classified by the three mode bits into 8 modes. In a VoIP stream, if two vectorsc 1 and C 2 are belong to different modes, the w, x, y and z bit of each will have different bit-weight respectively. In other words, if the mode of a vector is small, the value of thisvector and the bit-weight of the last four bits will both be small. 613

In compressed vector C, the bit-weights of last four bits are decided by mode bits and s is the notation bit. So it is not likely to hide information in these four bits, since any minor changes to them will cause a huge value loss. To clarify, in the following parts, we use Mode to denote the class of different speech vector values, and use w, x, y, and z bit to indicate the potential embedding bits. 2) The proposed VAMI Algorithm: From the previous analysis of G.711 codec, we find that, when two vectors C 1 and C 2 of different modes, assuming C 1 <C 2 ), the smaller one will have smaller bit-weight of w, x, y and z bit. That is to say, when we embed the same bit, for example bit w, C 1 will suffer less sound loss due to its smaller bit-weights. Because whether the potential embedding places available is decided by sound quality, availability of embedding places is decided by both mode and embedding places. Therefore, using MOS to estimate sound quality, we propose the following algorithm to judge the availability of potential embedding bit. Firstly, assume information embeds in the signal C of mode n and embedding bit t. Then we define the following function: Pesq(n, t) = MOS nt,wheremos nt denotes the MOS value according to the signal of mode n and embedding bit t. Furthermore, according to the MOS standard and practical demand, we set a threshold MOS value MOS thres.when MOS nt MOS thres, it indicates that such mode n and embedding bit t can satisfy the required quality. In other words, potential embedding bit t is available for vector of mode n. MOS 4.5 4 3.5 3 0 0.5 1 1.5 2 2.5 3 The Mode Index Figure 3. Relation between MOS and Mode Mode 0 Mode 1 Mode 2 Mode 3 the MOS value with different Modes Table IV POTENTIAL EMBEDDING PLACES Mode 0 1 2 3 Available potential embedding places w,x,y,z x, y, z y, z z Hiding information in higher bits distracts the statistical characters therefore the NVR detection is difficult to examine whether there are hidden data within the VoIP stream or not. Pesq(n, t) =MOS nt In order to find out all the available potential embedding places for each mode, we conduct the following experiment. We embed information in w, x, y and z of mode from 0 to 7 then evaluate their MOS value and bandwidth respectively. The results are illustrated in figure. 3 and table. III. Table III BANDWIDTH OF DIFFERENT MODES Figure 4. The VAMI Mode 0 1 2 3 4 5 6 7 bps 3221.6 1764.6 1638.5 1164.6 359.7 41.7 1.4 0 According to the results and MOS standard, we use Fig. 3 to demonstrate the available potential embedding bits for each mode.the result of mode 4, 5, 6 and 7 are not shown in result due to their rare appearance in VoIP stream. In Fig. 3, smaller mode owns a wider range of available embedding bits and broader bandwidth. On the other hand, higher modes have less choice and narrow bandwidth. In all, we list the all the available potential embedding places in table Table IV. B. VADDI In VAMI, although the embedding place is dynamically decided, the embedding intervals are still fixed. Fixing embedding intervals bring vulnerability to anti-detecting ability and will be suffered from more loss of sound quality. To tackle this issue, we proposed VAD, based on NVR detection condition, dynamically change embedding intervals. Firstly, we study the different results by embedding in silence and conversation. Then we propose VAD algorithm to judge whether the communication is in conversation. Further, 614

Figure 5. A sample for different features within silent and active voices according to HAS (Human Audio System) covering effect [7], we dynamically decide the embedding intervals. 1) the NVR Detection Analysis: As it introduced in related work, NVR detection demands following conditions to detect embedding information: Incept enough speech packets. NVR relies on a basic fact, in a large file,the ratio between even vectors and odd vectors will approximately be 1. Thus, unless incept enough packets, NVR detection will misjudge many common packets to be suspicious. Close value. Referring to a certain audio stream, NVR detection also requires lot value-close speech packets. The more value-close packets the larger NVR distortion will be. To study the voice activity features, we measure the waveform for different voices streams in different environments, and the results are shown in Fig. 5. As indicated in the left waveform the value swings in small scales for silence voices, while in the right waveform the value changed frequently for active VoIP communications. 2) Enhancing anti-detecting ability: From the previous analysis, we find out that by embedding information during the conversation period will enhance the anti-detecting ability. To be specific, in VADDI, we use VAD (Voice Activity Detection) to decide when to embed information. In other words we choose embedding intervals based on communication period. Firstly, since lots of value-close packets appear in silence,that is to say, when we embedding information in silent period of VoIP stream, it will show an obvious statistic character and therefore can be easily discovered the stream is embedded with information. However, when we embed information during the conversation, the value-close speech packets may rarely appear. Plus, due to the uncertainty of the length of the conversation, it is difficult for NVR detection to intercept enough speech packets. Moreover, according to the HAS (Human Audio System), it is easier for human to discover the noise in silent environment. Since the embedding process will inevitably generate sound distortion, use VAD to guide embedding information during conversation will improve the sound quality. Hence, it is getting even harder for eavesdropper to notice the VoIP steganography is running. 3) the VADDI algorithm: When applying VADDI, we firstly should use VAD to judge whether the VoIP stream is in conversation period. We separate the stream into frames and use frames as the units for VAD. Each frame consists of certain amount of vectors. The size of frame will affect the accuracy and efficiency of VAD algorithm. Containing fewervectors will result in higher acuteness in value changes. On the other hand, more vectors will result in higher accuracy in judging value changes. Based on G.711 codec, we choose the frame of 40 speech vectors. After acquiring the frames, on the basis of requirements of increasing anti-detecting ability, VADshould follow listedconditions to examine the communication status, Detect value-close neighbor vectors to sabotage NVR detection. Detect high value frequencies to apply HAS-based embedding. Effective and simple algorithm to reduce the high latency. 4) Extracting and calculating VAD feature value : Conversation demonstrates an interesting fact that within the conversation period the value oscillates around the zero periodically. Thus we denote the speech packet threshold value L which means packet above L can be used to embed information. Then denote the number of half waves above L as n. By referring to the first VAD requirement, the bigger n indicates greater difference between neighbor vectors. Then if n is bigger than zero, it indicates high value appears in this frame. Therefore, according to the second requirement, the information can be embedded in. Moreover, n can be used as the feature value of VAD. Let the VAD algorithm as P and input frames as frame i. Then frame i can be denoted as frame i =< x 1,x 2,..., x 40 >, where x i represents speech vectors. P (frame i ) {true, false}, where {truefalse} denotes voice activity status. The process of calculation of n works as follows. Firstly, for frame i find out its longest sub-sequence frame sub =< x i1, x i2,..., x ik >. Thereafter, x ik frame sub indicates x ik >Land x ik x i(k+1) < 0(1 k<n). Then, the threshold N can be calculated as P (frame i )= { true iff n > N false iff n N When P (frame i ) is true, it indicates the following frame i+1 is suitable for embedding. If P (frame i ) is false, the information will stop embedding in theframe i+1. In Fig 6, half waves are recognized. If n 5,thenwave indicates the conversation is running. In other words, the next frame can be embedded. (1) 615

0.8 0.6 0.4 1.05 1.04 The anti detection results Origin AVIS VAMI LSB L0.2 1.03 0 0.2 L X0 X1 X2 X3 X4 X5 X6 X7 X8 X9 NVR 1.02 1.01 0.4 1 0.6 0.99 1.226 1.228 1.23 1.232 1.234 1.236 1.238 1.24 1.242 x 10 4 0.98 0 5 10 15 20 25 The Audio Sample Index Figure 6. The wave detection results Figure 7. The Anti-Detecting Test results A. Experiment setup IV. EXPERIMENTAL RESULTS To evaluate the performance of AVIS, we implemented our algorithms in Linphone, a famous open-source VoIP platform. Moreover, according to VoIP testing standard (ITU-T Rec. P.830) [10], we choose 25 audio samples from the famous Open Speech Repositories. We intercept the VoIP stream of receiver and use the Emiprix Call Analyzer and the Pesq tool to evaluate performance of AVIS. We evaluate the metrics of anti-detecting performance, voice quality, latency and bandwidth. For AVIS parameters, we set L = 5 and N = 2. Meanwhile, due to HAS covering effect, the embedding intervals was set to 0 in order to expand the bandwidth which means all vectors in conversation frame can be embedded. After all, if a frame indicates the conversation is running, the next frame which contains 40 vectors will all be embedded with information. B. Anti-detection capability In the anti-detecting test, we focus on the neighbor vectors ratio in LSB steganography, VAMI method and AVIS strategy. In figure 7, the purple, green, red and blue lines represents the LSB steganography, VAMI method, AVIS strategy and original audio wave, respectively. All vectors of original audio waves value are lower than 1.01. Hence, we choose 1.01 as threshold value which means the communication is suspicious of using steganography if its value above threshold.to be specific, probability of failure of detection of LSB steganography is 80%, VAMI with 20% and AVIS strategy with only 8%. The result demonstrates that using VAMI can distract the statistical character caused by information embedding. Further, using AVIS strategy can dynamically choose the embedding intervals and when to embed. As a result, using AVIS strategy can effectively protect the information from NVR detection. C. MOS Value Test MOS 4.5 4.4 4.3 4.2 4.1 4 3.9 3.8 3.7 3.6 The MOS results 3.5 0 5 10 15 20 25 The Audio Sample Index Figure 8. The MOS Value results Any unmodified G.711 coded audio will be qualified as 4.5. We intercept the speech packets of the receiver and use PESQ to calculate the MOS. With all 25 samples are above 4.0, we hold the point that it is hardly possible for eavesdropper SNR(Signal-to-Noise Ratio) detection to notice steganography is used in VoIP. D. The Latency Test In the test, though applying AVIS strategy will occupies one third of the process time. However, with regard to latency of Internet up to 20ms, such increasing in encoding and decoding process will not be perceivable by humans. 616

Latency(S) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 AVIS(encode) Origin(encode) AVIS(decode) Origin(decode) China (973 project) under Grant No.2009CB320503; the National 863 Development Plan of China under Grant No. 2009AA01A334, 2009AA01A346, 2009AA01Z423; and the project of National Science Foundation of China under grant No. 61070199, 61003301, 60903223, 60903224; and the Supported by Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education, the Innovative Research Team in University of Hunan Province; and the University student Innovation project of Hunan Provience. REFERENCES [1] N. Provos and P. Honeyman, Hide and seek: An introduction to steganography, IEEE Security and Privacy, vol. 1, pp. 32 44, May 2003. Figure 9. The Latency Test results [2] K. Bailey and K. Curran, An evaluation of image based steganography methods, Multimedia Tools Appl., vol. 30, pp. 55 88, July 2006. E. The Bandwidth Test We use the 25 samples as input and get average bandwidth of 114.08 Bps. Although it seems somewhat narrow, it is enough for secret information transmission. F. The security concerns of AVIS Information security comes from anti-detecting ability and anti-attacking ability. Since N represents the changes in vectors, the anti-detecting performance is mainly related with half waves threshold N. Fig. 10 indicates the relationship between NVR and N. The bigger the N leads to NVR closer to 1 which means the higher anti-detecting ability. NVR 1.008 1.007 1.006 1.005 1.004 1.003 1.002 1.001 1 1 1.5 2 2.5 3 3.5 4 4.5 5 No. of Half waves Figure 10. The Security results ACKNOWLEDGMENT The work described in this paper is partially supported by the grants of the National Basic Research Program of [3] C. Wang and Q. Wu, Information hiding in real-time voip streams, in Proceedings of the Ninth IEEE International Symposium on Multimedia, ser. ISM 07. Washington, DC, USA: IEEE Computer Society, 2007, pp. 255 262. [4] W. Mazurczyk and K. Szczypiorski, Steganography of voip streams, in Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems, ser. OTM 08. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 1001 1018. [5] N. Aoki, A technique of lossless steganography for g.711 telephony speech, in Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, ser. IIH-MSP 08. Washington, DC, USA: IEEE Computer Society, 2008, pp. 608 611. [6] H. Tian, K. Zhou, Y. Huang, D. Feng, and J. Liu, A covert communication model based on least significant bits steganography in voice over ip, in Proceedings of the 2008 The 9th International Conference for Young Computer Scientists. Washington, DC, USA: IEEE Computer Society, 2008, pp. 647 652. [7] H. Tian, K. Zhou, H. Jiang, and D. Feng, Digital logic based encoding strategies for steganography on voice-over-ip, in Proceedings of the 17th ACM international conference on Multimedia, ser. MM 09. New York, NY, USA: ACM, 2009, pp. 777 780. [8] G. T. Juan You, Statistics detection algorithm based on audio lsb steganography, Computer Engineering of CHINA, vol. 35(24), pp. 176 177, 2009. [9] C. Wang, improved voip security with real-time speech hiding in g.711, Ph.D. dissertation, National Chi Nan University, 2007. [10] N. Harada, Y. Kamamoto, T. Moriya, and et.al, Emerging itu-t standard g.711.0 - lossless compression of g.711 pulse code modulation, in ICASSP, 2010, pp. 4658 4661. 617