Chapter 2 Literature Survey

Transcription

1 Chapter 2 Literature Survey

2 Chapter 2 Literature Survey Introduction Several digital watermarking techniques are proposed which includes watermarking for images, audio and video. The watermarking is primarily developed for the images [1-11], the research in audio is started later. There are less watermarking techniques are proposed for audio compared to the images/video. Embedding the data in audio is difficult compared to the images because the Human auditory system (HAS) is more sensitive than the Human visual System (HVS). In last ten years there is a lot of advancement in audio watermarking few of them are discussed here. This chapter reviews the literature of information hiding in audio sequences. Scientific publications included into the literature survey have been chosen in order to build a sufficient background that would help out in identifying and solving the research problems. During the last decade audio watermarking schemes [12-50] have been applied widely. These schemes are sophisticated very much in terms of robustness and imperceptibility. Robustness and imperceptibility are important requirements of watermarking, while they conflict each other. Non-blind watermarking schemes are theoretically interesting, but not so useful in practical use, since it requires double storage capacity and communication bandwidth for watermark detection. Of course, non-blind schemes may be useful for copyright verification mechanism in a copyright dispute. On the other hand, blind watermarking schemes can detect and extract watermarks without use of the unwatermarked audio. Therefore it requires only a half storage capacity and half bandwidth compared with the non-blind watermarking scheme. Hence, only blind audio watermarking methods are considered in this chapter. 2.1 Spread Spectrum Audio Watermarking Most of the existing audio watermarking techniques embed the watermarks in the time domain/ frequency domain where as there are few techniques which embed the data in cepstrum or compress domain. Spread spectrum (SS) technique is most popular technique and is used by many researchers in their implementations [12-18]. Amplitude scaled Spread Spectrum Sequence is embedded in the host audio signal which can be detected via a correlation technique. Generally embedding is based on a 13

3 psychoacoustic model (which provides inaudible limits of watermark). Watermark is spread over a large number of coefficients and distortion is kept below the just noticeable difference level (JND) by using the occurrence of masking effects of the human auditory system (HAS). Change in each coefficient can be small enough to be imperceptible because the correlated detector output still has a high signal to noise ratio (SNR), since it dispreads the energy present in a large number of coefficients. Boney et al [12] generated watermarks by filtering a pseudo noise sequence with a filter that approximates the frequency masking characteristics of the HAS. Thus the different watermarks are created for different audio signal. Their study and results show that their scheme is robust in the presence of additive noise, lossy coding/decoding, resampling and time scaling. They also state that using their scheme it is easy to detect the watermark for the author. However they have used the original signal to detect the watermark. The scheme is also robust in presence of other watermarks. J. Seok et al [13] proposed a novel audio watermarking algorithm which is based on a direct sequence spread spectrum method. The information that is to be embedded is modulated by a pseudo noise (PN) sequence. The spread spectrum signal is then shaped in the frequency domain and inserted into the original audio signal. To detect the watermark they used linear predictive coding method. Their experimental results show that their scheme is robust against different signal processing attacks. D. Kirovski et al [14] developed the techniques which effectively encode and decode the direct sequence spread spectrum watermark in audio signal. They have used the modulated complex lapped transform to embed the watermark. To prevent the desynchronization attack they developed the technique based on block repetition coding. Though they have proved that they can perform the correlation test in perfect synchronization, the wow and flutter induced in watermarked signal may cause false positive/false negative detection of watermark. To improve the reliability of watermark detection they proposed the technique which uses cepstrum filtering and chess watermarks. They have also shown that psychoacoustic frequency masking creates an imbalance in the number of positive and negative watermark chips in the part of the SS sequence that is used for correlation detection which corresponds to the audible part of the frequency spectrum. To compensate this problem they propose a modified covariance test. 14

4 Malvar et al [15] introduces a new watermarking modulation techniques called as Improved Spread Spectrum (ISS) technique. This scheme proposes a new embedding approach based on traditional SS embedding by slightly modifying it. In this scheme they introduced two parameters to control the distortion level and control the removal of carrier distortion on the detection statistics. At a certain values of these control parameters traditional SS can be obtained from this scheme. S. Esmaili et al [16] presented a novel audio watermarking scheme based on spread spectrum techniques that embeds a digital watermark within an audio signal using the instantaneous mean frequency (IMF) of the signal. This content-based audio watermarking algorithm was implemented to satisfy and maximize both imperceptibility and robustness of the watermark. They used short-time Fourier transform of the original audio signal to estimate a weighted IMF of the signal. Based on the masking properties of the psychoacoustic model the sound pressure level of the watermark was derived. Based on these results then modulation is performed to produce a signal dependent watermark that is imperceptible. This method allows 25 bits to embed and recover within a 5 second sample of an audio signal. Their experimental results show that the scheme is robust to common signal processing attacks including filtering, and noise addition whereas the Bit error rate (BER) increased to 0.08 for mp3 compression i.e. 2 out of 25 bits where not identified. D. Kirovski et al [17] devised a scheme for robust covert communication over a public audio channel using spread spectrum by imposing particular structures of watermark patterns and applying nonlinear filter to reduce carrier noise. This technique is capable to reliably detect watermark, even in audio clips modified using a composition attacks that degrade the content well beyond the acceptable limit. Hafiz Malik et al [18] proposed an audio watermarking method based on frequency selective direct sequence spread spectrum. The method improves the detection capability, watermarking capacity and robustness to desynchronization attacks. In this scheme the process of generating a watermark and embedding it into an audio signal is treated in the framework of spread spectrum theory. The original signal is treated as noise whereas the message information used to generate a watermark sequence is considered as data. The spreading sequence also called as PNsequence is treated as a key. The technique introduces lower mean square as well as perceptual distortion due to the fact that a watermark is embedded in a small frequency band of complete audible frequency range. 15

5 2.2 Methods using patchwork algorithm The patchwork technique was first presented in 1996 by Bender et al [19] for embedding watermarks in images. It is a statistical method based on hypothesis testing and relying on large data sets. As a second of CD quality stereo audio contains samples, a patchwork approach is applicable for the watermarking of audio sequences as well. The watermark embedding process uses a pseudorandom process to insert a certain statistic into a host audio data set, which is extracted with the help of numerical indexes (like the mean value), describing the specific distribution. The method is usually applied in a transform domain (Fourier, wavelet, etc.) in order to spread the watermark in time domain and to increase robustness against signal processing modifications [19]. Multiplicative patchwork scheme developed by Yeo et al [20] provides a new way of patchwork embedding. The Most of the embedding schemes are additive such as y=x+αw, while multiplicative embedding schemes have the form y=x (1+αw). Additive schemes shift average, while multiplicative schemes changes variance. Thus detection scheme exploits such facts. In this scheme first mean and variance of the sample values are computed in order to detect the watermarks, second they assume that distribution of the sample values is normal, third they try to decide the value of detection threshold adaptively. Cvejic et al [21] presented a robust audio watermarking method implemented in wavelet domain which uses the frequency hopping and patchwork method. Their scheme embeds the watermark to a mapped sub band in a predefined time period similar to frequency hopping approach in digital communication and detection method is modified patchwork algorithm. Their results show that the algorithm is robust against the mp3 compression, noise addition, requantization and resampling. For this system to be robust against the resampling attack it is required to find out the proper scaling parameter. R. Wang et al [22] proposed an audio watermarking scheme which embedded robust and fragile watermark at the same time in lifting wavelet domain. Robust watermark is embedded in the low frequency range using mean quantization. It had great robustness and imperceptibility. Fragile watermark is embedded in the high frequency range by quantizing single coefficient. When the audio signal is tampered, the watermark information will change synchronously. So it can be used for audio 16

6 content integrity verification. The watermark can be extracted without the original digital audio signal. Their experimental results show that robust watermark is robust to many attacks, such as mp3 compression, low pass filtering, noise addition, requantization, resampling and so on whereas fragile watermark is very sensitive to these attacks. 2.3 Methods implemented in Time Domain There are few algorithms implemented in time domain [23-26]. These algorithms embed the watermarks in the host signal in time domain by modifying the selected samples. W. Lie et al [23] proposed a method of embedding digital watermarks into audio signals in the time domain. Their algorithm exploits differential average-ofabsolute-amplitude relations within each group of audio samples to represent one-bit information. The principle of low-frequency amplitude modification is employed to scale amplitudes in a group manner (unlike the sample-by-sample manner as used in pseudo noise or spread-spectrum techniques) in selected sections of samples so that the time-domain waveform envelope can be almost preserved. Besides, when the frequency-domain characteristics of the watermark signal are controlled by applying absolute hearing thresholds in the psychoacoustic model, the distortion associated with watermarking is hardly perceivable by human ears. The watermark can be blindly extracted without knowledge of the original signal. Subjective and objective tests reveal that the proposed watermarking scheme maintains high audio quality and is simultaneously highly robust to pirate attacks, including mp3 compression, lowpass filtering, amplitude scaling, time scaling, digital-to-analog/analog-to-digital reacquisition, cropping, sampling rate change, and bit resolution transformation. Security of embedded watermarks is enhanced by adopting unequal section lengths determined by a secret key. In a method suggested by Bassia et al [24] watermark embedding depends on the audio signals amplitude and frequency in a way that minimizes the audibility of the watermark signal. The result is a slight amplitude modification of each audio sample in a way that does not produce any perceived effect. The audio signal is divided in N s segments of N samples each. Each of these segments is watermarked with the bipolar sequence Wi { 1,1 },i = 0,1,2...N 1, which is generated by 17

7 thresholding a chaotic map. The seed (starting point) of the chaotic sequence generator is the watermark key. By using generators of a strongly chaotic nature ensures that the system is cryptographically secure, i.e., the sequence generation mechanism cannot be inverse engineered even if an attacker can manage to obtain a part of the binary sequence. The watermark signal is embedded in each audio segment using the following three-stage procedure. The signal-dependent, low-pass-shaped watermark signal is embedded in the original signal segment to produce the watermarked signal segment. This scheme is statistically imperceivable and resists MPEG2 audio compression plus other common forms of signal manipulation, such as cropping, time shifting, filtering, resampling and requantization. A. N. Lemma et al [25] investigated an audio watermarking system is referred to as modified audio signal keying (MASK). In MASK, the short-time envelope of the audio signal is modified in such a way that the change is imperceptible to the human listener. In MASK, a watermark is embedded by modifying the envelope of the audio with an appropriately conditioned and scaled version of a predefined random sequence carrying some information (a payload). On the detector side, the watermark symbols are extracted by estimating the short-time envelope energy. To this end, first, the incoming audio is subdivided into frames, and then, the energy of the envelope is estimated. The watermark is extracted from this energy function. The MASK system can easily be tailored for a wide range of applications. Moreover, informal experimental results show that it has a good robustness and audibility behavior. 2.4 Methods implemented in Transform domain Synchronization attack is one of the key issues of digital audio watermarking. In this correspondence, a blind digital audio watermarking scheme against synchronization attack using adaptive quantization is proposed by X.Y. Wang et al [26]. The features of the their scheme are as follows: 1) a kind of more steady synchronization code and a new embedded strategy are adopted to resist the synchronization attack more effectively; 2) the multiresolution characteristics of discrete wavelet transform (DWT) and the energy-compression characteristics of discrete cosine transform (DCT) are combined to improve the transparency of digital watermark; 3) the watermark is embedded into the low frequency components by adaptive quantization according to human auditory masking; and 4) the scheme can extract the watermark without the help of the original digital audio signal. Experiment 18

8 results shows that the proposed watermarking scheme is inaudible and robust against various signal processing attacks such as noise adding, resampling, requantization, random cropping, and MPEG-1 Layer III (mp3) compression. Barker code has better self-relativity, so Huang et al. [27] chooses it as synchronization mark and embeds it into temporal domain and embeds the watermark information into DCT domain. It can resist synchronization attack effectively. But it has such defects as follows: 1) it chooses a 12-bit Barker code which is so short that it is easy to cause false synchronization; 2) it only embeds the synchronization code by modifying individual sample value, which reduces the resisting ability greatly (especially against resampling and mp3 compression); 3) it does not make full use of human auditory masking effect. S. Wu, J. Hang. et al [28] proposed a self-synchronization algorithm for audio watermarking to facilitate assured audio data transmission. The synchronization codes are embedded into audio with the informative data, thus the embedded data have the self-synchronization ability. To achieve robustness, they embed the synchronization codes and the hidden informative data into the low frequency coefficients in DWT (discrete wavelet transform) domain. By exploiting the time-frequency localization characteristics of DWT, the computational load in searching synchronization codes has been dramatically reduced, thus resolving the contending requirements between robustness of hidden data and efficiency of synchronization codes searching. The performance of the scheme is analyzed in terms of SNR (signal to noise ratio) and BER (bit error rate). An estimation formula that connects SNR with embedding strength has been provided to ensure the transparency of embedded data. BER under Gaussian noise corruption has been estimated to evaluate the performance of the proposed scheme. The experimental results are presented to demonstrate that the embedded data are robust against most common signal processing and attacks, such as Gaussian noise corruption, resampling, requantization, cropping, and mp3 compression. Li et al [29] developed the watermarking technique in wavelet domain based on SNR to determine the scaling parameter required to scale the watermark. The intensity of embedded watermark can be modified by adaptively adjusting the scaling parameter. The authors have proved that the scheme is robust against different signal processing attacks and provide better embedding degree. This scheme requires the original signal to recover the watermark. This motivates us to develop the SNR based 19

9 scheme to detect and extract the watermark without using the original signal. The watermark embedding procedure adaptively selects the watermark scaling parameter α for each of the section of audio segment selected for embedding. A new watermarking technique capable of embedding multiple watermarks based on phase modulation is devised by A. Takahashi et al [30]. The idea utilizes the insensitivity of the human auditory system to phase changes with relatively long transition period. In this technique the phase modulation of the original signal is realized by means of a time-varying all-pass filter. To accomplish the blind detection which is required in detecting the copy control information, this watermark is assigned to the inter-channel phase difference between a stereo audio signal by using frequency shift keying. Meanwhile, the copyright management information and fingerprint are embeds in to both channels by using phase shift keying of different frequency components. Consequently these three kinds of information are simultaneously embedded into a single time frame. The imperceptibility of the scheme is confirmed through a subjective listening test. The technique is robust against several kinds of signal processing attacks evaluated by computer simulations. Author found that their method has good performance in both subjective and objective tests. H. H. Tsai et al [31] proposed a new intelligent audio watermarking method based on the characteristics of the HAS and the techniques of neural networks in the DCT domain. The method makes the watermark imperceptible by using the audio masking characteristics of the HAS. Moreover the method exploits a neural network for memorizing the relationships between the original audio signals and the watermarked audio signals. Therefore the method is capable of extracting watermarks without original audio signals. Their experimental results show that the method significantly possesses robustness to be immune against common attacks for the copyright protection of digital audio. C. Xu et al [32] implemented a method to embed and extract the digital compressed audio. The watermark is embedded in partially uncompressed domain and the embedding scheme is high related to audio content. The watermark content contains owner and user identifications and the watermark embedding and detection can be done very fast to ensure on-line transactions and distributions. X. Li et al [33] developed a data hiding scheme for audio signals in cepstrum domain. Cepstrum representation of audio can be shown to be very robust to a wide 20

10 range of attacks including most challenging time-scaling and pitch shifting warping. The authors have embedded the data by manipulating statistical mean of selected cepstrum coefficients. Psychoacoustic model is employed to control the audibility of introduced distortion. S. K. Lee et al [34] suggested a watermarking algorithm in cepstrum domain. They insert a digital watermark into the cepstral components of the audio signal using a technique analogous to spread spectrum communications, hiding a narrow band signal in a wideband channel. The pseudorandom sequence used as watermark is weighted in the cepstrum domain according to the distribution of cepstral coefficients and the frequency masking characteristics of human auditory system. Watermark embedding minimizes the audibility of the watermark signal. The technique is robust against multiple watermark, MPEG coding and noise addition. There are various techniques implemented in wavelet domain [35-41]. In these papers it is proved that the wavelet domain is the more suitable domain compare to the other transform domains. As the wavelet coefficients contain the multiple spectrums of multiple band frequencies, this transform is more suitable than other transform domains to select the perceptible band of frequencies for data embedding. 2.5 Other recently developed algorithms: Audio watermarking is usually used as a multimedia copyright protection tool or as a system that embed metadata in audio signals. In the method suggested by S. D. Larbi et al [42] watermarking is viewed as a preprocessing step for further audio processing systems: the watermark signal conveys no information, rather it is used to modify the nonstationarity. The embedded watermark is then added in order to stationnarize the host signal. Indeed the embedded watermark is piecewise stationary, thus it modifies the stationarity of the original audio signal. In some audio processing this can be used to improve the performances that are very sensitive to time variant signal statistics. The authors have presented the analysis of perceptual watermarking impact on the stationarity of audio signals. Their study was based on stationarity indices, which represented a measure of variations in spectral characteristics of signals, using time-frequency representations. They had presented their simulation results with two kinds of signals, artificial signals and audio signals. They had observed the significant enhancement in stationarity indices of watermarked signal, especially for transient attacks. 21

11 T. Furon et al [43] investigated an asymmetric watermarking method as an alternative to direct sequence spread spectrum technique (DSSS) of watermarking. This method is developed to provide higher security level against malicious attacks threatening watermarking techniques used for a copy protection purpose. This application, which is quite different from the classical copyright enforcement issue is extremely challenging as no public algorithm is yet known to be secure enough and some proposed proprietary techniques are already hacked. The asymmetric detectors need more complexity and more money and they accumulate bigger amount of content in order to take decision. Conventional watermarking techniques based on echo hiding provide many benefits, but also have several disadvantages, for example, lenient decoding process, weakness against multiple encoding attacks etc. B.S. Ko et al [44] improve the weak points of conventional echo hiding by time-spread echo method. Spreading an echo in the time domain is achieved by using pseudo-noise (PN) sequences. By spreading the echo the amplitude of each echo can be reduced, i.e. the energy of each echo becomes small, so that the distortion induced by watermarking is imperceptible to humans while the decoding performance of the embedded watermarks is better maintained as compared with the case of conventional echo hiding method. Authors have proved this by computer simulations, in which several parameters, such as the amplitude and length of PN sequences and analysis window length, were varied. Robustness against typical signal processing was also evaluated in their simulations and showed fair performance. Results of listening test using some pieces of music showed good imperceptibility. S. Eerüçük et al [45] introduced a novel watermark representation for audio watermarking, where they embed linear chirps as watermark signals. Different chirp rates, i.e. slopes on time-frequency plane, represent watermark messages such that each slope corresponds to a unique message. These watermark signals, i.e. linear chirps, are embedded and extracted using an existing watermarking algorithm. The extracted chirps are then post processed at the receiver using a line detection algorithm based on the Hough-Radon transform (HRT). The HRT is an optimal line detection algorithm, which detects directional components that satisfy a parametric constraint equation in the image of a TF plane, even at discontinuities corresponding to bit errors. The simulations carried by authors showed that HRT correctly detects the embedded watermark message after signal processing operations for bit error rates 22

12 up to 20%. The new watermark representation and the post processing stage based on HRT can be combined with existing embedding/extraction algorithms for increased robustness. A new adaptive blind digital audio watermarking algorithm is proposed by X. Wang et al [46] on the basis of support vector regression (SVR). This algorithm embeds the template information and watermark signal into the original audio by adaptive quantization according to the local audio correlation and human auditory masking. During the watermark extraction the corresponding features of template and watermark are first extracted from the watermarked signal. Then, the corresponding feature template is selected as training sample to train SVR and an SVR model is returned. Finally the actual outputs are predicted according to the corresponding feature of watermark, and the digital watermark is recovered from the watermarked audio by using the well-trained SVR. The algorithm is not only robust against various signal processing attacks but also has high perceptibility. The performance of the algorithm is better than other SVM audio watermarking schemes. 2.6 Audio watermarking techniques against time scale modification Synchronization attacks are a serious problem to any watermarking schemes. Audio processing such as random cropping and time scale modification (TSM) causes displacement between embedding and detection in time domain and is difficult for watermark to survive. TSM is a serious attack to audio watermarking, very few algorithms can effectively resist this kind of synchronization attack. According to the Secure Digital Music Initiative (SDMI) Phase-II robustness test requirement, practical audio watermarking schemes should be able to withstand pitch-invariant TSM up to ±4%. Mansour and Twefik [47] proposed to embed watermark by changing the relative length of the middle segment between two successive maximum and minimum of the smoothed waveform, the performance highly depends on the selection of the threshold and it is a delicate work to find an appropriate threshold. Mansour and Twefik [48] proposed another algorithm for embedding data into audio signals by changing the interval lengths between salient points in the signal. The extreme point of the wavelet coefficients from the selected envelop is adopted as salient points. 23

13 W. Li et al [49] have suggested a novel content dependent localized scheme to combat synchronization attacks like random cropping and time-scale modification. The basic idea is to first select steady high-energy local regions that represent music edges like note attacks, transitions or drum sounds by using different methods, then embed the watermark in these regions. Such regions are of great importance to the understanding of music and will not be changed much for maintaining high auditory quality. In this way the embedded watermark will have the potential to escape all kinds of distortions. Experimental results carried out by authors show that the method is highly robust against some common signal processing attack and synchronization attack. This method has its inherent limitations. Although it is suitable for most modern music with obvious rhythm, it does not work with some classical music without apparent rhythm. S. Xiang et al [50] presented a multibit robust audio watermarking solution by using the insensitivity of the audio histogram shape and the modified mean to TSM and cropping operations. Authors have addressed the insensitivity property in both mathematical analysis and experimental testing by representing the histogram shape as the relative relations in the number of samples among groups of three neighboring bins. By reassigning the number of samples in groups of three neighboring bins, the watermark sequence is successfully embedded. In the embedding process, the histogram is extracted from a selected amplitude range by referring to the mean in such a way that the watermark will be able to be resistant to amplitude scaling and avoid exhaustive search in the extraction process. They observed that the watermarked audio signal is perceptibly similar to the original one. Experimental results demonstrated by authors prove the robustness of the scheme against TSM and random cropping attacks and has a satisfactory robustness for those common signal processing attacks. A blind digital audio watermarking scheme against synchronization attack using adaptive mean quantization is developed by X-Y. Wang et al [51]. The features of the scheme are as follows 1) a kind of more steady synchronization code and a new embedded strategy are adopted to resist the synchronization attack more effectively; 2) the multiresolution characteristics of DWT and energy-compression characteristics of discrete cosine transform are combined to improve the transparency of digital watermark 3) the watermark is embedded into the low frequency components by adaptive quantization according to human auditory masking; and 4)the scheme can 24

14 extract the watermark without the help of original audio signal. The experimental result added in the paper show that the technique can resist the various signal processing attacks. 2.7 Papers studied on performance analysis and evaluation of watermarking systems Powerful and low cost computers allow people to easily create and copy multimedia content, and the Internet has made it possible to distribute this information at very low cost. However, these enabling technologies also make it easy to illegally copy, modify, and redistribute multimedia data without regard for copyright ownership. Many techniques have been proposed for watermarking audio, image, and video, and comprehensive surveys of these technologies is presented in previous sections. However, it is required to consider an effective means of comparing the different approaches. J. D. Gordy et al [52] have presented an algorithm independent set of criteria for quantitatively comparing the performance of digital watermarking algorithms. Four criterions were selected by authors as a part of the evaluation framework. They were chosen to reflect the fact that watermarking is effectively a communications system. In addition, the criteria are simple to test, and may be applied to any type of watermarking system (audio, image, or video). 1) Bit rate refers to the amount of watermark data that may be reliably embedded within a host signal per unit of time or space, such as bits per second or bits per pixel. A higher bit rate may be desirable in some applications in order to embed more copyright information. Reliability was measured as the bit error rate (BER) of extracted watermark data. 2)Perceptual quality refers to the imperceptibility of embedded watermark data within the host signal. In most applications, it is important that the watermark is undetectable to a listener or viewer. This ensures that the quality of the host signal is not perceivably distorted, and does not indicate the presence or location of a watermark. The signal-to-noise ratio (SNR) of the watermarked signal versus the host signal was used as a quality measure. 3) Computational complexity refers to the processing required to embed watermark data into a host signal, and / or to extract the data from the signal. Actual CPU timings (in seconds) of algorithm implementations were collected. 4) Watermarked digital signals may undergo common signal 25

15 processing operations such as linear filtering, sample requantization, D/A and A/D conversion, and lossy compression. Although these operations may not affect the perceived quality of the host signal, they may corrupt the watermark data embedded within the signal. It is important to know, for a given level of host signal distortion, which watermarking algorithm will produce a more reliable embedding. Robustness was measured by the bit error rate (BER) of extracted watermark data as a function of the amount of distortion introduced by a given operation. The performance of spread-transform dither modulation watermarking in the presence of two important classes of non additive attacks, such as the gain attack plus noise addition and the quantization attack are evaluated by F. Bartolini et al [53]. The authors developed the analysis under the assumption that the host features are independent and identically distributed Gaussian random variables, and a minimum distance criterion is used to decode the hidden information. The theoretical bit-error probabilities are derived in closed form, thus permitting to evaluate the impact of the considered attacks on the watermark at a theoretical level. The analysis is validated by means of extensive Monte-Carlo simulations. In addition to the validation of the theoretical analysis, Monte-Carlo simulations permitted to abandon the hypothesis of normally distributed host features, in favor of more realistic models adopting a Laplacian or a generalized Gaussian probability density function. The general result of the analysis carried out by authors is that the excellent performance of ST-DM is confirmed in all cases with only noticeable exception of the gain attack. Hidden copyright marks have been proposed as a solution for solving the illegal copying and proof of ownership problems in the context of multimedia objects. Many systems have been proposed by different authors but it was difficult to have idea of their performance and hence to compare them. Then F.A.P. Petitcolas et al [54] propose a benchmark based on a set of attacks that any system ought to survive. G.C. Rodriguez et al [55] presented a survey report on audio watermarking in which watermarking techniques are briefly summarized and analyzed. They have made the following observations: The patchwork scheme and cepstrum domain scheme are robust to several signal manipulations, but for real applications authors suggest to use patchwork scheme because the cepstrum domain scheme needs the original 26

16 signal to determine that the host signal is marked as a consequence it needs the double storage capacity. The echo hiding scheme only fulfill with the inaudibility condition and is not robust to several attacks such as mp3 compression, filtering, resampling, etc. In early September 2000 Secure Digital Music Initiative (SDMI) announced a three-week open challenge for its phase II screening, inviting the public to evaluate the attach resistance for four watermark techniques. The challenge emphasized on testing the effectiveness of robust watermarks, which is crucial in ensuring the proper functioning of the entire system. M. Wu et al [56] points out weaknesses in these watermark techniques and suggest directions for further improvement. Authors have provided the general framework for analyzing the robustness and security of audio watermark systems. 2.8 Watermark Attacks Research in digital watermarking has progressed along two paths. While new watermarking technologies are being developed, some researchers are also investigating different ways of attacking digital watermarks. Some of the attacks that have been proposed in the literature are reviewed here. Frank Hartung et al [57] have shown that the spread spectrum watermarks and watermark detectors are vulnerable to a variety of attacks. However with appropriate modifications to the embedding and extraction methods, methods can be made much more resistant to variety of such attacks. Frank et al classified the attack in four groups: a) Simple attack attempt to impair the embedded watermark by manipulation of the whole watermarked data. b) Detection disabling attacks attempt to break the connection and to make the recovery of watermark infusible or infeasible for a watermark detector. c) Ambiguity attacks attempt to analyze the watermarked data, estimate the watermark or host data, separate the watermarked data into host data and watermark, and discard only the watermark. Frank Huntung et al [57] also suggested the counter attack to those attacks. Martin Kutter et al [58] suggested the watermark copy attack, which is based on an estimation of the embedded watermark in the spatial domain through a filtering process. The estimate of the watermark is then adapted and inserted into the target image. To illustrate the performance of the proposed attack they applied it to 27

17 commercial and non-commercial watermarking schemes. The experiments showed that the attack is very effective in copying a watermark from one image to a different image. Alexander et al [59] suggested the watermark template attack. This attack estimates the corresponding template points in the FFT domain and then removes them using local interpolation. The approach is not limited to FFT domain; other transformation domains may also exploit similar variants at this attack. J. K. Su et al [60] suggested a Channel Model for a Watermark Attack. Authors have analyzed this attach for images and stated that the attack can be applied to audio/video watermarking schemes. D. Kirovski et al [61] analyzed the security of multimedia copyright protection systems that use watermarks by proposing a new breed of attacks on generic watermarking systems. A typical blind pattern matching attack relies on the observation that multimedia content is often highly repetitive. Thus the attack procedure identifies subsets of signal blocks that are similar and permutes these blocks. Assuming that permuted blocks are marked with distinct secrets, it can be shown that any watermark detector is facing a task of exponential complexity to reverse the permutations as a preprocessing step for watermark detection. Authors have described the implementation of attack against a spread-spectrum and a quantization index modulation data hiding technology for audio signals. 2.9 Research problems identified: The problems identified from the literature survey carried out in this chapter include: 1) Construction of the method that would identify perceptually significant components from an analysis of image/audio and the Human visual system/ Human auditory system. 2) The system must be tested against lossy operations such as mp3 and data conversion. The experiments must be expanded to validate the results. 3) There is a need to explore novel mechanisms for effective encoding and decoding of watermark using DSSS in audio. The technique may aim at improving detection convergence and robustness, improving watermark imperceptiveness. Preventive attacks such as desynchronization attack and possibility of establishing covert communication over a public audio channel. 28

18 4) Possible asymmetric watermark method may be an alternative to classical DSSS watermarking, which may provide higher security level against malicious attacks. 5) Possible generation of a framework for blind watermark detection. 6) Possibility of suggesting new malicious attacks and counter attack for available watermarking techniques. 7) Possibility of embedding audio watermark in audio and design an adaptive system to overcome number of non-intentional attacks. Concluding remarks: Chapter 2 reviews the literature and describes the concept of information hiding in audio sequences. Scientific publications included in the literature survey have been chosen in order to build a sufficient background that would help out in better understanding of the research topic. A survey of the key digital audio watermarking algorithms and techniques presented are classified by the signal domain in which the watermark is inserted and statistical method used for embedding and extraction of watermark bits. Audio watermarking initially started as a sub-discipline of digital signal processing, focusing mainly on convenient signal processing techniques to embed additional information to audio sequences. This included the investigation of a suitable transform domain for watermark embedding and schemes for imperceptible modification of the host audio. Only recently has watermarking been placed to a stronger theoretical foundation, becoming a more mature discipline with a proper base in both communication modeling and information theory. My research concentrates on developing an audio watermarking technique to detection convergence and robustness, improving watermark imperceptiveness. An attempt is also made to embed the audio data in audio signal during this research. 29