Transparent and Robust Audio Data Hiding in Subband Domain

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1 Transparent and Robust Audio Data Hiding in Subband Domain Xin Li 1* and Hong Heather Yu 2 1 Dept. of Electrical Engineering, Princeton University, Princeton NJ Panasonic Information and Networking Technologies Laboratory, Panasonic Technologies Inc., Princeton NJ Abstract Data hiding embeds extra information into digital media for the purpose of authentication, annotation and copyright protection. Transparency and robustness are two contradictory requirements for any data hiding scheme. This paper studies audio data hiding using spread spectrum(ss) technique in subband domain. On transparency part, we propose a novel method of tuning psycho-acoustic models employed in audio compression to control the audibility of introduced distortion for data hiding purpose. On robustness part, we have studied feature selection and synchronization problem in order to maximize the survivability of the embedded data. Our study shows that mid-band coefficients are appropriate features for data embedding. And we propose to track /recover synchronization of audio signal before the detection, facilitating the extraction of the embedded data. Experiment results have shown that our data hiding scheme in subband domain can survive a wide range of attacks while providing transparent audio quality and abundant embedding capacity(>20bps). 1. Introduction Rapid evolution of digital world has greatly facilitated manipulation and transmission of digital media signals including audio, image and video. However easy access and dupli-cation has posed serious problem of piracy in media distribution. The technique referred to as data hiding [1] has been proposed for copyright protection as well as authentication and annotation. Transparency and robust-ness are two contradictory requirements for any data hiding scheme. Meanwhile, embedding capacity is also an important requirement in some specific application scenario. The above three requirements have put a lot of challenges to the researchers on audio data hiding problem. First of all, transparency requires that * This work was performed while the author was a summer intern at Panasonic Information and Networking Technologies Laboratory embedded data should not introduce any audible distortion to the host audio signal. However, HAS(Human Auditory System) has a wide dynamic and differential range[1], which is obviously a huge barrier to the data hiding problem. Secondly, various audio processing software are widely available on the internet and can be freely downloaded. They include most common signal processing tools(e.g. lowpass/bandpass filtering, addition of echo and time-scale warping). Those operations often bring little degradation on audio quality but could seriously affect the extraction of embedded data. They have shown to be the most challenging attacks to audio data hiding schemes. Thirdly, due to limited bandwidth of audio signal(a few orders less than that of video signal), the capacity requirement is nontrivial either. For example, in order to embed an active agent like Java Applet into a segment of audio signal(16bit, 44,100Hz) with normal length(3~5 minutes), it is generally thought that the data hiding scheme should provide the capacity of at least 20bps, which means at least 1 bit per 2,205 samples. In order to meet the above challenges, several audio watermarking/data hiding schemes have been proposed in recent years. Echo hiding [2] is one of the most wellknown audio data hiding techniques. Motivated by the fact that HAS cannot distinguish an echo from the original when delay and amplitude of the echo are appropriately controlled, D Gruhl et al. at MIT propose to employ two different delay times to carry binary information. Researchers at UMN study robust audio watermarking using perceptual masking [3],[4](it can be viewed as embedding only 1 bit information into the audio). P. Bassia and I. Pitas have studied robust audio watermarking in time domain [5]. Unlike former schemes, this paper proposes a novel approach of embedding secondary data into audio signal in subband domain. In contrast to the original time domain, subband decomposition of audio signal provides several advantages which can help us to achieve the requirements of transparency, robustness and capacity.

2 Firstly, psycho-acoustic models in subband domain have been successfully applied to audio compression, especially MPEG-I standard on audio coding part[6], [7]. The success mainly attributes to their capability of making use of auditory masking property of HAS to control the audibility of quantization noise. Obviously, those parametric models could also be used to control the audibility of introduced distortion for the problem of embedding secondary data into the host signal. Secondly, subband coefficients, which have physical meaning in frequency domain, are more robust to various attacks than original audio samples. For example, a simple jittering attack might significantly change the structure of audio samples in time domain, but its subband decomposition experiences much less disturbance, which provides us the opportunity of achieving a scheme with better robustness than the former ones in time domain. We present a general framework for audio data hiding following the classical Spread Spectrum(SS) approach [8]. SS technique has been widely applied to image watermarking/data hiding [9]~[11] before. Here, we make a systematic study of its application in audio scenario and propose a novel transparent and robust audio data hiding scheme in subband domain. Inside the above framework, we focus on two important issues affecting the robustness of audio data hiding scheme: feature selection and synchronization problem. Feature selection. Which coefficients are selected as features to embed the data plays important role on the robustness performance. Intuitively, low-band coefficients are not suitable for embedding because they are perceptually significant and can not experience large distortion. High-band coefficients are not suitable either because they are too sensitive to introduced distortion by attacks. A natural choice is to select mid-band coefficients. Through empirical tests, we have found an appropriate boundary of defining mid-band to maximize the survivability of embedded data over a wide range of attacks. synchronization problem. Another issue which needs special consideration in audio scenario is synchronization (alignment between the test audio and the original audio on a frame-by-frame basis). A simple shift of the whole audio signal by several samples brings little sacrifice on audio quality but could severely affect the detection performance. This is because almost all data hiding schemes embed data on a frame-by-frame basis. Synchronization uncertainty introduced by any attack would affect the alignment of frames and makes it much more difficult to extract embedded bits and recover embedded data correctly. In order to solve such synchronization uncertainty problem, we propose to classify common attacks into two types according to their effect on the synchronization structure of audio signal: 1) Type-I: It includes MPEG compression, lowpass/ bandpass filtering, additive/multiplicative noise, addition of echo and resampling/requantization. This type of attack does not significantly change the synchronization structure of audio but only shift the whole sequence by some unknown number of samples. 2) Type-II: It includes jittering, time-scale warping, pitch-shift warping and down/up sampling. This type of attack would destroy the synchronization structure of the audio and is generally believed much more challenging than type-i attack. For type-i attacks, we propose to track the synchronization of audio signal using a unique signature: SYNCH tag. The SYNCH tag is inserted into audio signal at owner-specified location using pre-masking/post-masking property of HAS in time domain. The detector would search such tag first to resolve the synchronization uncertainty. For type-ii attack, when the detection is not totally blind(with some prior information e.g. the length of original audio or embedding capacity), we propose to recover synchronization by time-scale modification [13]. Synchronization recovering can greatly increase the robustness of the data hiding scheme. On transparency part, based on our own empirical studies we propose an effective solution of tuning existing psychoacoustic models used in MPEG compression to fit our data hiding purpose. The reason why they can not be directly used lies in the fundamental difference between data compression and data hiding. In audio compression, the energy of quantization noise is always bounded by that of the host signal. While in data hiding, the energy of introduced distortion could be arbitrarily large as long as it is kept masked. Therefore we follow the original approach to calculate the SMR(Signal-to-Masking Ratio) for each subband and derive the corresponding scaling factors. But we truncate the derived results using some pre-selected threshold to guarantee the inaudibility of introduced distortion. Objective measurement shows that the SNR result of audio signal with embedded data produced by our scheme is comparable to that of audio signal given by MP3 compression at 64kbps. The rest of this paper is organized as following. Section 2.1 presents a general framework for audio data hiding in subband domain. Section 2.2 studies feature selection to maximize the robustness of our proposed data hiding scheme. Section 2.3 addresses the synchronization problem and proposes to recover synchronization structure of audio signal before the detection. Section 2.4 details on tuning psychoacoustic models to control the audio quality. Section 3 uses extensive experiment results to support the superiority of our scheme in terms of transparency and robustness. Finally some conclusion is given in Section 4.

3 2. Audio Data Hiding in Subband Domain Subband decomposition of audio signal can be implemented by polyphase transform. Here we apply the polyphase transform described in MPEG-I audio coding part [6],[7]. The forward transform maps each group(with size 32) of audio samples to a group of subband coefficients and the inverse transform maps them back to the time domain. Although the original audio samples can not be perfectly reconstructed, introduced errors are indeed negligible. Subband decomposition of audio signal provides several advantages over original time-domain representation for our data hiding purpose. On transparency side, there exists psycho-acoustic models in subband domain which we can employ to control the introduced distortion. On robustness side, subband coefficients, which have physical meaning in frequency domain, often experience much less variation than original audio samples after various attacks. In this section, we shall present a general framework for audio data hiding in subband domain first and then study the feature selection and synchronization problem in order to maximize the robustness. The psychoacoustic model for transparency requirement will be covered at the end. 2.1 A General Framework Our general framework for audio data hiding looks very similar to the one proposed in [11] for image watermarking. Figure 2.1 shows the diagram of embedding(encoder) and extracting(decoder) process. x(n) T embedded data {1,0,1,1,0, } extracted data {1,0,1,1,0, } X(n) Analysis Enc. Dec. Y(n) key Z(n) Figure 2.1 Encoder/Decoder Diagram Spread-Spectrum(SS) technique inserts a unique signature (typically a pseudo-random sequence generated by a secret key) and uses the existence of the signature to carry 1 bit extra information. Suppose every single bit is embedded into every single frame of N samples. In subband domain, assume I i (i=1,,n) is the selected features(n subband coefficients out of N) and encoder works as following: T -1 T y(n) Attac z(n) embed 1 : I i =I i + a i W i ; embed 0 : I i =I i - a i W i (1) where a i is controlled by psychoacoustic model and W i is owner-specified signature. For oblivious detection where original signal is not available, decoder formulates the extraction as a binary hypothesis testing problem: H 1 : X i =I i + a i W i + N i H 0 : X i =I i - a i W i + N i (2) The correlation of X i and W i is chosen as testing statistics q: q = (3) Under the assumption that W i is independent of I i and N i, we have Σ I i W i E{I i W i } = E{I i }E{W i } = 0 (4) Σ N i W i E{N i W i } = E{N i }E{W i } = 0 (5) Then for large n, q approximately observes normal distribution centered at S = (6) under assumption H 1 and centered at S under assumption H 0. Therefore, by setting the threshold to be zero, we can tell whether 1 or 0 is embedded. Inside this framework, we continue to discuss how to maximize the robustness of the data hiding scheme. We focus on two important problems: at the encoder how to select appropriate features for data embedding and at the decoder how to correctly extract the embedded data. 2.2 Feature Selection n i= 1 n i= 1 X i W i a i W i At encoder side, given the embedding strategy, the main issue affecting the robustness of data hiding scheme is which bands of coefficients should be selected as features. Among all 32 subbands, not all of them are suitable for data embedding purpose. Intuitively, low bands contain most perceptually significant coefficients and allow little distortion, i.e. signature power defined in (6). For the first several low bands, their SMR (Signal-to- Masking Ratio) is typically above 10dB, which means allowed distortion must be 10dB lower than the signal energy. In such situation low-band coefficients behave like a strong noise term in testing statistics q. Therefore it is very likely that the term of Σ I i W i would deviate largely from zero and causes possible wrong decision. It follows 2

4 that low-band coefficients are not suitable for data embedding. On the other hand, high-bands contain least perceptually significant coefficients and allow relatively large distortion. However, high bands are most sensitive to the noise introduced by common signal processing operations(e.g. lossy compression and low-pass filtering). Therefore noise terms N i typically has larger variance and might even correlate with inserted signature W i. In such situation, the term Σ N i W i would deviate largely from zero and affects the detection performance. It follows that highband coefficients are not suitable for data embedding either. Based on the above observations, it is natural to consider selecting only mid-band coefficients, say k-th bands (K 1 <k<k 2 ), as the features to embed the data. The boundary of mid-band can be determined from SMR curves. Through empirical studies we have found that K 1 =4,K 2 =16 is a good choice in the sense of giving nearly maximal survivability over a wide range of attacks. 2.3 Synchronization Recovering At the decoder, the testing statistics q is computed on a frame-by-frame basis and its sign denotes the extracted information. Due to the limitation of such frame-based embedding approach, there is a special issue which plays vital role on the correct extraction of embedded data: synchronization, i.e. the alignment between the marked audio 1 and the original audio. Although synchronization problem also exists in image/video data hiding, it requires serious consideration in audio scenario because of audio signal s one dimensional character. Most audio processing operations would shift the whole audio sequence by a random number of samples, introducing some synchronization uncertainty at the decoder. Such uncertainty has little effect on audio quality but could have catastrophic effect on the detection performance. Once the frame-byframe alignment goes wrong, the decoder is more likely to get wrong decision. And even if the bit stream is correctly extracted, its own synchronization still needs to be solved fort the reason of recovering embedded data correctly. We propose to resolve the synchronization uncertainty at the decoder before the extraction. As we mentioned in the introduction, common attacks on audio signal can be classified into two categories according to their effect on synchronization structure. Here we address them in detail and present our solutions to each category of attacks separately. 1) Type-I: synchronization of the audio can be tracked. This class of attacks only shift the whole sequence by some random number of audio samples. Therefore, the synchronization structure of the marked audio can still be tracked and uncertainty can be solved at the decoder. Type-I attacks include MPEG-I layer-3(i.e. MP3) coding 1 we use marked audio to denote the audio with embedded data /decoding, lowpass & bandpass filtering, additive& multiplicative noise, addition of echo and resampling &requantization. For type-i attacks, we propose to track the synchronization of host audio by inserting a SYNCH tag in time domain. SYNCH tag is a unique pseudorandom sequence and inserted into a segment before or after a sound using pre-masking/post-masking property [12] of HAS. The exact location of SYNCH tag can be specified by the owner and kept as a secret key to protect it from intentional attack. Decoder would detect SYNCH tag first and make necessary alignment before extracting the data. 2) Type-II: Synchronization cannot be tracked. This class of attacks would destroy the synchronization structure of the marked audio and make synchronizationtracking impossible. The number of total samples may or may not be changed. Type-II attacks include jittering, time-scale warping, pitch-shift warping and resampling. It has been generally believed that type-ii attacks are more challenging than type-i attacks. A simple jittering attack can easily destroy most existing audio watermarking/data hiding schemes [3]~[5] with little sacrifice on subjective quality. To fight against such synchronization-destroying attacks, we propose to recover synchronization structure under the assumption that some information about the original audio (e.g. its length or embedding capacity) has been registered and the decoder can rely on such prior information to help the data extraction. For example, if the decoder finds the length of audio has been cut into half, it would interpolate it back to the original length and then do the extraction. Our studies have shown that for most type-ii attacks, synchronization structure can be at least partially recovered by time-scale modification technique [13], which greatly increases the robustness of our scheme. 2.4 Psychoacoustic Model The transparency requirement is fulfilled by employing a parametric psychoacoustic model in subband domain. It is easy to see that introduced distortion is directly controlled by the scaling factor a i in (1). The function of psychoacoustic model is to compute SMR(Signal-to-Masking Ratio) for each band and accordingly tune each a i. The validity of psychoacoustic model arises from the masking property of HAS, i.e. a secondary sound can be masked (made inaudible) due to the appearance of a major sound. This property has been widely employed in audio compression such as perceptual coding. And obviously if the encoder in our data hiding scheme can be well controlled by an accurate psychoacoustic model, the marked audio would be subjectively indistinct from the original one in spite of the embedded extra information. Psychoacoustic models [12] in either time or frequency domain have been widely studied in various areas of

5 audio signal processing [14]. For example, so-called Psychoacoustic Model I&II [6],[7] are used to control the quantizer design and bit allocation in the audio coding part of MPEG-I standard. Typically psychoacoustic calculation consists of following seven steps [6]: 1) Time-to-Frequency mapping by Fourier Transform; 2) Process spectral values in groupings related to critical band widths; 3) Separate spectral values into tonal and non-tonal components; 4) Apply a spreading function; 5) Set a lower bound for the threshold values; 6) Find the masking threshold for each subband; 7) Calculate signal-to-mask ratio(smr). For every frame of audio samples, the above procedure would produce a set of SMR values, which can be used to control the quantizer stepsize. The success of psychoacoustic model I&II in compression scenario motivates us to tune them for the data hiding purpose. The main difference between data compression and data hiding lies in the mechanism of introduced distortion. In data compression, distortion is introduced as quantization noise and controlled by a rate-distortion optimization process. The variance of the quantization noise is always bounded by that of the host signal itself when no bit is allocated. However, in data hiding, distortion is introduced when inserting a signature and controlled only by psycho-acoustic model. The variance of the introduced distortion might outrun that of the host signal as long as it is kept inaudible. Although the masking thresholds produced by the above psychoacoustic calculation satisfy the need for quantization purpose in compression, we find they are not suitable for controlling the energy of introduced distortion in data hiding. In particular, empirical studies have shown that they generate audible distortion in high bands. To tune the above calculation steps for data hiding, we propose to make the following modification with step 6)~7) based on our subjective listening tests: 6) Find the masking threshold LT min for each band and truncate it if it is above a pre-selected threshold Th m. Th m is typically 30~40dB for transparency requirement(this range is chosen for the nearly optimal tradeoff between the transparency and the robustness). 7) Calculate scaling factor for k-th band: a k = w10log10 ( LTmin ( k) / 20) where w is a weight between 0 and 1 proportional to the energy of current frame. Subjective quality of the marked audio generated by the above psychoacoustic model is almost identical to that of the original one, which justifies the efficiency of our tuning method. 3. Experiment Results We pick up two pieces of typical audio signal(16bit, 44,100Hz) as our test signals: a segment of classical music(11.6s) and the first part of Tom s Diner by Suzanne Vega (20.7s). The second piece is consider to be difficult to handle in data hiding because it contains many silence segments. Embedded data are chosen to be the copyright informa-tion and the lyric information. Embedded data are mapped to a binary bit stream first. Encoder embeds 1 bit into each frame containing 1152 samples, which gives embedding capacity of around 38bps at sampling rate of 44,100Hz. Polyphase transform and psychoacoustic model calculation are both updated on a frame-by-frame basis. Owner-specified secret keys specify the following information: signature number, location of SYNCH tag and embedding capacity. At the decoder, SYNCH tag is extracted first to track the synchronization. If a SYNCH tag can not be found, we employ time-scale modification technique to recover the synchronization structure of the marked audio with prior information such as embedding capacity or the duration of original audio. 1) Transparency Initial subjective listening tests have shown that most ordinary audience can not distinguish the difference between the original audio signal and the marked audio signal. To further objectively demonstrate that we have fulfilled the transparency requirement, we compare the SNR(Signal-to-Noise Ratio) result between the marked audio and the decoded audio given by MP3 compression at different bit rate. It is generally agreed that MP3 compression at the rate of 64kbps provides transparent audio quality. As we can see from table 1, our audio data hiding scheme does offer very close SNR performance to MP3 compression at 64kbps. MP3(kbps) Ours SNR(dB) Table 1. SNR comparison between MP3 compression and data hiding 2) Robustness We use detection probability to describe the detection performance of our proposed scheme. Detection probability(p D ) is defined to be the ratio of the number of correctly-extracted bits to the overall embedded bits(in total, around 1,200 bits are embedded to two segments of audio signals). Table 2 contains the detection performance after MP3 compression at various rates. We can see that embedded data can be completely extracted for MP3 compression at the rate of above 48kbps. Rate(kbps) P D Table 2. Robustness to MP3 coding/decoding

6 Table 3 shows the detection performance after low-pass filtering with different cut-off frequency. It can be seen from table 3 that our data hiding schemes have shown very good robustness to low-pass filtering. f_ cut (Hz) 20k 10k 8k 4k P D Table 3. Robustness to low-pass filtering Table 4 includes the detection probability result after band-pass filtering. Again, we can observe very good survivability of embedded data even after serious bandpass filtering operation. f_low, f_high(hz) 40,20k 250,10k 1k,4k P D Table 4. Robustness to band-pass filtering Table 5 demonstrates the robustness performance of proposed scheme after addition of echo. Although echo brings noticeable distortion to audio signal, our scheme still shows pretty good robustness to this class of attacks. Delay(ms), Volume(%) 100,10 300,30 500,50 P D Table 5. Robustness to addition of echo Above attacks all belong to type-i and our scheme has shown good robustness over all of them. Most formerlyproposed audio watermarking/data hiding schemes have shown to be very sensitive to type-ii attacks. So we have tested the robustness of our scheme for synchronizationdestroying attacks: jittering and time-scale warping. a(%) P D Table 6. Robustness to jittering attack speed P D Table 7. Robustness to time-scale warping Table 6 and 7 have shown the detection probability after those attacks(synchronization recovering has been applied to the test signal after time-scale warping). Our proposed scheme still demonstrates good survivability even after serious jittering attack(as much as 10%). Unfortunately only partial information can survive the challenging timescale warping attack. But it is interesting to notice that in some trivial cases(e.g. speed=0.8 1/speed=1.25) when synchronization is easy to recover, embedded data can be completely extracted. In short, our proposed scheme has shown to be very robust over a wide range of attacks except the most challenging time-scale warping attack. We have successfully embedded a Java Applet into a piece of long audio signal for playback/record control. The complexity of encoder and decoder are both kept very low(comparable to MP3 compression), which facilitates its application in practice. 4. Conclusion In this paper, we studied transparent and robust audio data hiding techniques. Data is embedded by inserting a unique signature in subband domain. Transparency requirement is achieved by employing a psychoacoustic model to control introduced distortion. Robustness requirement is achieved by appropriately choosing features at the encoder and resolve synchronization uncertainty at the decoder. The data hiding scheme we developed can provide transparent audio quality, good survivability over a wide range of attacks and abundant embedding capacity. [Reference] [1] W. Bender, D.Gruhl, N. Morimoto, A. Lu, Techniques for Data Hiding, IBM System Journal Vol.35 No.3-4 [2] D Gruhl, A. Lu and W. Bender, Echo Hiding, Information Hiding 96, pp [3] L. Boney, A.H.Tewfik and K.N.Hamdy, Digital Watermarks for Audio Signals, IEEE ICMCS 96, pp [4] M.D.Swanson et al., Robust Watermarking using Perceptual Masking, Signal Processing Vol.66 No.3, pp [5] P.Bassia and I. Pitas, Robust Audio Watermarking in the Time Domain, EUSIPCO 98, pp [6] Davis Pan, A Tutorial on MPEG/Audio Compression, IEEE Multimedia Journal 1995 summer issue [7] ISO/IEC , Information Technology-Coding of Moving Pictures and Associated Audio for Digital Storage Media at up to about 1.5Mbit/s, Part 3: Audio [8] Ingemar J. Cox et al., Secure Spread Spectrum Watermarking for Multimedia, IEEE Trans. On Image Processing, Dec 1997, pp [9] C. I. Podilchuk and W. Zeng, Image-Adaptive Watermarking Using Visual Models, IEEE Journal on Selected Areas in Communication, Vol.16, No.4 May 1998 [10] A. Piva et al., DCT-based Watermark Recovering without Resorting to the Uncorrupted Original Image, ICIP 97, Vol.III, pp [11] W.Zeng and Bede Liu, A Statistical Watermark Detection Technique without Using Original Images for Resolving Rightful Ownership of Digital Images, IEEE Trans. On Image Processing, 1999 [12] E. Zwicker and H. Fastl, Psychoacoustics: Facts and Models, Springer-Verlag 1990 [13] W.B.Kleijn and K.K. Paliwal, Speech Coding and Synthesis, Elsevier 1995

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