- Research
- Open Access
An imperceptible and robust audio watermarking algorithm
- Ali Al-Haj^{1}Email author
https://doi.org/10.1186/s13636-014-0037-2
© Al-Haj; licensee Springer. 2014
- Received: 17 April 2014
- Accepted: 18 September 2014
- Published: 9 October 2014
Abstract
In this paper, we propose a semi-blind, imperceptible, and robust digital audio watermarking algorithm. The proposed algorithm is based on cascading two well-known transforms: the discrete wavelet transform and the singular value decomposition. The two transforms provide different, but complementary, levels of robustness against watermarking attacks. The uniqueness of the proposed algorithm is twofold: the distributed formation of the wavelet coefficient matrix and the selection of the off-diagonal positions of the singular value matrix for embedding watermark bits. Imperceptibility, robustness, and high data payload of the proposed algorithm are demonstrated using different musical clips.
Keywords
- Audio watermarking
- Copyright protection
- Discrete wavelet transform
- Singular value decomposition
- Imperceptibility
- Robustness
- Data payload
11 Introduction
The recent advancements of digital audio technology have increased the ease with which audio files are stored, transmitted, and reproduced. However, along with such conveniences come new risks such as copyright violation. Conventional encryption algorithms permit only authorized users to access encrypted digital data; however, once decrypted, there is no way to prohibit illegal copying and distribution of the data [1]. A promising solution to the copyright violation problem is to apply audio watermarking in which audio files are marked with secret, robust, and imperceptible watermarks to achieve copyright protection [2]-[5]. Indeed, a digital watermark is a good deterrent to illicit copying and dissemination of copyrighted audio since it can provide evidence of copyright infringements after the copyright violation has occurred.
Audio watermarking techniques which are used for copyright protection of digital audio signals must satisfy two main requirements: imperceptibility and robustness [6]. Imperceptibility refers to the condition that the embedded watermark should not produce audible distortion to the sound quality of the original audio. That is, the watermarked version of the audio signal must be indistinguishable from the original audio signal. On the other hand, robustness ensures the resistance of the watermark against removal or degradation. The watermark should survive malicious attacks such as random cropping and noise adding. Some watermarking applications may demand additional requirements such as high data payload and low computational time of the watermarking algorithm [3]. In practice, there exists a fundamental trade-off between the different watermarking requirements.
Audio watermarking can be carried out in the time domain or the transform domain of the audio signal. Time-domain techniques based on least significant bit substitution and echo hiding are found extensively in literature [7]-[12]. In general, time-domain audio watermarking techniques are relatively easy to implement and require few computing resources. However, they are less robust than transform-domain techniques which employ the human perceptual properties and frequency masking characteristics of the human auditory system [13]. Popular transforms that have been widely used in digital watermarking include the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the discrete wavelet transform (DWT), and the singular value decomposition (SVD) [14]-[20].
It has been reported recently that imperceptible and robust audio watermarking can be achieved by applying a cascade of two different transforms on the original audio signal. Being different, the cascaded transforms may provide different, but complementary, levels of robustness against the same attack. Many audio watermarking techniques based on hybrid transforms have been proposed in literature. These techniques include but are not limited to DWT-DCT [21], DWT-SVD [22], and SVD-STFT [23].
Several hybrid algorithms based on the SVD transform have been recently proposed in literature. In the algorithm proposed by [23], the audio signal is first converted into a matrix form using the short-time Fourier transform (STFT), the SVD transform is then applied on the matrix, and finally embedding is carried out by adaptively modifying the SVD coefficients with watermark bits. In the hybrid algorithm proposed by [24], the audio signal is partitioned into blocks, and the watermark bits are embedded using dither modulation quantization of the singular values of the blocks. In [23], an audio watermarking algorithm is proposed in which watermark embedding and extraction procedures are based on the quantization of the norms of the singular values of audio blocks. The same authors proposed in [25] a hybrid algorithm in which watermark bits are embedded by applying quantization index modulation (QIM) on the singular values of wavelet-domain blocks. All of the abovementioned SVD-based hybrid algorithms employ some sort of quantization to embed watermark bits. Although quantization is simple, an acceptable level of robustness against noise and filtering attack may not always be achieved.
In this paper, we propose a semi-blind hybrid audio watermarking algorithm based on the DWT and SVD transforms. In the proposed algorithm, the audio signal is sampled, partitioned into short audio segments called frames, and a four-level DWT decomposition is applied on each frame. A matrix is then formed by arranging the wavelet coefficients of all detail sub-bands in a unique distributed pattern which scatters the watermark bits throughout the transformed frame to provide a high degree of robustness. The SVD operator is then applied on the matrix, and the watermark bits are embedded onto the off-diagonal zero elements of the S matrix produced by the SVD transform. Unlike the other SVD-based algorithms, the proposed algorithm leaves the non-zero singular values of the S matrix unchanged to ensure high watermarking imperceptibility.
The rest of the paper is organized as follows. In the next section, the DWT and SVD transforms are described, and their unique utilization in the proposed algorithm is outlined. The proposed audio DWT-SVD watermarking algorithm is described in detail in Section 3, and evaluated with respect to imperceptibly, robustness, and data payload in Section 4. Concluding remarks are given in Section 5.
22 Related work and contribution
The proposed algorithm is based on cascading the two transforms: DWT and SVD. The uniqueness of the proposed algorithm is twofold: the distributed formation of the DWT coefficient matrix and the selection of the off-diagonal positions of SVD's singular value matrix for embedding watermark bits. Description of the two transforms and their exact utilization in the proposed algorithm is given in this section.
2.1 2.1 DWT-based audio watermarking
Many DWT-based audio watermarking algorithms can be found in literature. Many variations among the different algorithms exit; however, the main variation is in the sub-band chosen for embedding the watermark bits. In [27]-[29], the approximation sub-band is used for embedding the watermark bits, while in most algorithms, only one detail sub-band is used to embed the watermark bits [30]-[36]. Claims of good imperceptibility and robustness have been reported using the two embedding approaches.
2.2 2.2 SVD-based audio watermarking
The SVD transform has been used in several audio watermarking algorithms [22]-[25],[37]-[39]. The algorithms varied in the way the singular values were used in the watermarking process. For example, in [37], the single largest singular value, σ_{ 11, } was quantized and used to embed the watermark, whereas in [38], the encrypted watermark signal was added to all singular values of matrix Σ. In [22],[24],[25], the norms of all singular values were quantized and used in the watermark embedding process.
In our proposed algorithm, matrix A represents the detail sub-bands matrix shown in Figure 2, which is produced after applying DWT on the original audio signal. After applying the SVD operator on the DWT matrix, watermark bits are embedded onto the off-diagonal zero elements of the S matrix, while the diagonal singular values of the matrix remain unchanged. This embedding procedure will eliminate the possibility of any distortion caused to the singular values which may affect imperceptibility and watermarking quality. Related preliminary works have been published by the author and others in [40],[41]. The algorithms reported in those papers have low capacity as they embed the watermark bits in the single largest singular value, σ_{ 11 }, and not in the off-diagonal zero elements of the Σ matrix, as it is the case in the proposed algorithm.
33 Proposed DWT-SVD audio watermarking algorithm
In this section, we describe the proposed DWT-SVD algorithm. The algorithm consists of two procedures: watermark embedding and watermark extraction procedures.
3.1 3.1 Watermark embedding procedure
Step 2: Sample the original audio signal at a sampling rate of 44,100 samples per second and partition the sampled file into N frames. The optimal frame length will be determined experimentally in such a way to increase data payload.
where S_{ w } is the watermarked S matrix, and α is the watermark intensity which should be chosen to tune the trade-off between robustness and imperceptibility. With this type of embedding, the singular values of D remain unchanged, and thus, audible distortion caused by modifying the singular values is avoided.
The matrices U_{ 1 } and V_{ 1 }^{ T } are stored for later use in the extraction process. This makes the proposed watermarking algorithm semi-blind, as the whole original audio frame is not required in the extraction process.
where matrix Σ′ is the original Σ matrix with the S sub-matrix replaced by the S_{ 1 } sub-matrix.
Step 10: Apply the inverse DWT operation on the D_{ w } matrix to obtain the watermarked audio frame.
Step 11: Repeat all previous steps on each frame. The overall watermarked audio signal is obtained by concatenating the watermarked frames obtained in the previous steps.
3.2 3.2 Watermark extraction procedure
Step 1: Obtain the matrix S_{ 1 }′ from each frame of the watermarked audio signal following the general steps presented in Figure 7.
Step 4: Construct the original watermark image by assembling the bits extracted from all frames.
44 Experimental results
Four-level DWT decomposition is applied on each frame using the Daubechies wavelet (db1). Using other wavelet types has a little effect on the performance, as it was observed experimentally. Values ranging from 1 to 5 were used for the watermark intensity α. However, the results reported in this paper were obtained when the intensity value was set to 3. In what follows, we present performance results of the proposed algorithm with respect to three metrics: imperceptibility, robustness, and data payload [42],[43].
4.1 4.1 Imperceptibility results
Imperceptibility ensures that the quality of the signal is not perceivably distorted and the watermark is imperceptible to listeners. To measure imperceptibility, different authors use different metrics; however, the most commonly used metrics are signal-to-noise ratio (SNR) and listening tests.
4.1.1 4.1.1 Signal-to-noise ratio
SNR values for different audio signals
Audio type | SNR_{dB} |
---|---|
Pop audio | 38.75 |
Instrumental audio | 39.02 |
Speech audio | 37.50 |
Average | 38.17 |
4.1.2 4.1.2 Listening tests
Subjective and objective grades for audio quality measurement
Audio quality | Subjective difference grade (SDG) | Objective difference grade (ODG) |
---|---|---|
Imperceptible | 5 | 0 |
Perceptible, but not annoying | 4 | −1.0 |
Slightly annoying | 3 | −2.0 |
Annoying | 2 | −3.0 |
Very annoying | 1 | −4.0 |
We employed a blind subjective listening test to estimate the audio quality of the watermarked signals. The listening test was performed repeatedly with five adults in a listening room equipped with audio testing and recording devices. A computer system running a special software was also used for computer-controlled presentation of the watermarked signals to the listeners and for recording their responses. Each person was presented with ten pairs of signals (original and watermarked) and then asked to give performance scores using the 5-grade impairment scale given in Table 1. The five persons listened to each pair of signals ten times and gave an average SDG value for each pair. The average grade for each pair submitted by all persons is considered the final grade for that particular pair of signals. The SDG averages obtained for the subjective listening tests are 4.67, 4.72, and 4.81 for the pop, instrumental, and speech signals, respectively. These values clearly indicate that imperceptibility has been achieved by the proposed audio watermarking algorithm.
SDG and ODG values for different audio signals
Audio type | SDG | ODG |
---|---|---|
Pop audio | 4.67 | −0.67 |
Instrumental audio | 4.72 | −0.71 |
Speech audio | 4.81 | −0.91 |
Average | 4.73 | −0.76 |
Imperceptibility results for different algorithms
Reference | Algorithm | SNR (average) | SDG (average) | ODG (average) |
---|---|---|---|---|
Wang and Zhao [21] | DWT-DCT based | 43.11 | - | - |
Xiang [27] | DWT based | 23.98 | - | −1.98 |
Fallahpour and Megias [30] | DWT based | 30.65 | - | −0.7 |
Bhat et al. [24] | SVD-DM based | - | 4.64 | −0.73 |
Bhat et al. [25] | SVD-DWT based | 24.37 | 4.46 | - |
Proposed algorithm | SVD-DWT based | 38.17 | 4.73 | −0.76 |
4.2 4.2 Robustness results
Watermarked audio signals may undergo signal processing operations such as linear filtering, lossy compression, among many other operations [46],[47]. Although these operations may not affect the perceived quality of the host signal, they may corrupt the watermark embedded within the signal. Two sets of attacks were performed to test the robustness of our proposed algorithm. The first set includes the following set of common signal processing operations: Gaussian noise addition, re-quantization, re-sampling, MP3 compression, low-pass filtering, and echo addition. The other set is the Stirmark® audio watermarking benchmark which includes a whole set of add, modify, and filter attacks [48],[49].
where l is the watermark length, W_{ n } is the n th bit of the embedded watermark, and W′_{ n } is the n th bit of the extracted watermark.
4.2.1 4.2.1 Common signal processing operations
- 1.
Additive white Gaussian noise: White Gaussian noise is added to corrupt the watermarked signal to SNR levels of 15_{dB} and 20_{dB}.
- 2.
Re-quantization: The 16-bit watermarked audio signal is re-quantized to 8 bits per sample and 24 bits per sample.
- 3.
Re-sampling: The watermarked signal, originally sampled at 44.1 kHz, is down-sampled to 22.05, 11.025, and 6 kHz.
- 4.
MP3 compression: The watermarked audio signal is compressed at different bit rates: 128, 96, 64, and 32 kbps.
- 5.
Low, high, and band-pass filtering: Filtering at different cutoff frequencies is applied to the watermarked signal.
- 6.
Echo addition: An echo signal with a delay of 100 ms and different decay rates are added the watermarked signal.
BER values for common signal processing operations
Attack type | Pop audio | Instrumental audio | Speech audio | Average BER |
---|---|---|---|---|
Gaussian noise (15_{dB}) | 0 | 0 | 0 | 0 |
Gaussian noise (20_{dB}) | 0 | 0 | 0 | 0 |
Re-sampling 22.05 | 0.0021 | 0.000 | 0.0363 | 0.0128 |
Re-sampling 11.025 | 0.0061 | 0.0011 | 0.0448 | 0.0173 |
Re-sampling 6 kHz | 0.0901 | 0.0330 | 0.0543 | 0.0591 |
Re-quantization 24 | 0 | 0 | 0 | 0 |
Re-quantization 8 | 0 | 0 | 0 | 0 |
MP3 compression 128 kbps | 0 | 0 | 0 | 0 |
MP3 compression 96 kbps | 0.0301 | 0.0541 | 0.0721 | 0.0430 |
MP3 compression 64 kbps | 0.0521 | 0.0841 | 0.0820 | 0.0727 |
MP3 compression 32 kbps | 0.0810 | 0.1410 | 0.2901 | 0.1707 |
Echo (delay 100 ms, decay 50%) | 1.1264 | 1.5932 | 1.878 | 1.5325 |
Echo (delay 100 ms, decay 40%) | 1.0536 | 1.5641 | 1.7330 | 1.4500 |
Low-pass filtering 8 kHz | 0.0972 | 0.1540 | 0.3168 | 0.1893 |
High-pass filtering 50 Hz | 0.2701 | 0.2810 | 0.5231 | 0.3580 |
Band-pass filtering (100 to 4,000 Hz) | 0.1080 | 0.132 | 0.2130 | 0.1510 |
Comparison between BER values of different transform-based algorithms
Algorithm | ||||||
---|---|---|---|---|---|---|
DWT based | DWT-DCT based | DWT based | SVD-QIM based | SVD-DWT based | SVD-DWT based | |
[[29]] | [[21]] | [[50]] | [[22]] | [[25]] | Proposed algorithm | |
Gaussian noise (20_{dB}) | 7.525 | 0.0115 | 0 | 0 | 0 | 0 |
Re-quantization 8 | 0 | 0 | 0 | 0 | 0 | 0 |
Re-sampling 22.05 | 0 | 0 | 0 | 0 | 2 | 0.0128 |
MP3 compression 64 kbps | 4.34 | 0 | 0.08 | 0.5615 | 0 | 0.0727 |
MP3 compression 32 kbps | 17.22 | 0.03525 | 0.67 | 2.2094 | 1 | 0.1707 |
Echo (delay 100 ms, decay 40%) | - | - | 5.83 | 3.955 (98, 41) | 2 (98, 41) | 1.450 |
Low-pass filtering 8 kHz | - | - | 0.97 | 0.3540 (11,025 Hz) | 0 (11,025 Hz) | 0.1893 |
4.2.2 4.2.2 Stirmark© attacks
BER values due to Stirmark ® attacks
Stirmark attack | Extracted watermark (pop) | Pop audio | Instrumental audio | Speech audio | Average BER |
---|---|---|---|---|---|
AddBrumm (55 Hz Sinus) |
| 0 | 0 | 0 | 0 |
AddSinus (3000 Hz sinus) |
| 0 | 0 | 0 | 0 |
AddNoise (20 dB level) |
| 0 | 0 | 0 | 0 |
Stat1 (statistical distortion) |
| 0 | 0 | 0 | 0 |
Stat2 (statistical distortion) |
| 0 | 0 | 0 | 0 |
Smooth1 (simple smoothing) |
| 0.80 | 1.40 | 0.36 | 0.853 |
Smooth2 (simple smoothing) |
| 0.65 | 1.34 | 0.29 | 0.760 |
Amplify (increases amplitude) |
| 0 | 0 | 0 | 0 |
Invert (phase shift 180°) |
| 0 | 0 | 0 | 0 |
Exchange (swaps samples) |
| 5.01 | 5.54 | 3.68 | 4.74 |
CutSamples (7 samples per 1,000) |
| 2.41 | 3.11 | 1.08 | 2.20 |
LSBZero (reset LSBs) |
| 0 | 0 | 0 | 0 |
ZeroCross (reset samples) |
| 0 | 0 | 0 | 0 |
ZeroRemove (removes 0 samples) |
| 0 | 0 | 0 | 0 |
Comparison between BER values due to Stirmark ® attacks
Algorithm | ||||||
---|---|---|---|---|---|---|
DCT based | SVD-STFT based | DWT based | DWT based | SVD-QIM based | SVD-DWT based | |
[[51]] | [[23]] | [[27]] | [[30]] | [[22]] | Proposed algorithm | |
AddBrumm (55 Hz Sinus) | 1.25 | 0 | 15.79 | 0 | - | 0 |
AddSinus (3,000 Hz sinus) | 0.77 | 0 | 0 | 0 | - | 0 |
AddNoise (20 dB level) | 0.78 | 0 | 5.875 | 0 | 0 | 0 |
Stat1 (statistical distortion) | 0 | 0 | 0 | 9 | 0.1831 | 0 |
Stat2 (statistical distortion) | 0 | 0 | - | - | 0.7324 | 0 |
Smooth1 (simple smoothing) | 0 | 0 | 0 | 14 | 2.0874 | 0.853 |
Smooth2 (simple smoothing) | 0 | 0 | 0 | - | 1.0986 | 0.760 |
Amplify (increases amplitude by 50%) | 0 | 0.375 | 0 | 0 | - | 0 |
Invert (phase shift 180°) | 52.42 | 0 | 0 | 0 | 0 | 0 |
Exchange (swaps samples) | 0 | 0 | 0 | - | 0 | 4.74 |
CutSamples (7 samples per 1,000) | 100 | 1.5 | 0 | - | - | 2.20 |
LSBZero (sets LSBs to 0) | 0 | 0 | - | 0 | - | 0 |
ZeroCross (reset samples) | 0 | 0 | 0 | - | 0 | 0 |
ZeroRemove (removes 0 samples) | 100 | 0 | 0 | - | - | 0 |
4.3 4.3 Data payload results
Effect of frame length on data payload
Frame length (samples) | ||||||||
---|---|---|---|---|---|---|---|---|
512 | 1,024 | 2,048 | 4,096 | 8,192 | 16,384 | 32,768 | 65,536 | |
Data payload (bps) | 1,032 | 516 | 258 | 129 | 64 | 32 | 16 | 8 |
As shown in the table, the payload increases as the frame length decreases. However, short-length frames degrade performance and result in unacceptable imperceptibility and robustness results. A frame length of 2,048 samples has been fixed and used to evaluate imperceptibly and robustness of the proposed algorithm.
55 Conclusions
In this paper, we proposed an imperceptible and a robust audio watermarking technique based on cascading two well-known transforms: the discrete wavelet transform and the singular value decomposition. The two transforms were used in a unique way that scatters the watermark bits throughout the transformed frame in order to achieve high degrees of imperceptibility and robustness. High data payloads were also achieved. The simulation results obtained were in total agreement with the requirements set by IFPI for audio watermarking, thus proving the effectiveness of the proposed algorithm.
Future research will focus on enhancing the proposed algorithm to resist de-synchronization attacks such as random cropping, pitch shifting, amplitude variation, time-scale modification, and jittering. Methods proposed in the literature that counter de-synchronization attacks include the all-list-search method, the combination of spread spectrum and spread spectrum code method, the self-synchronization strategy method, and the synchronization code method. Our approach will be based on embedding synchronization codes with the watermark bits so that the hidden data have the self-synchronization capability.
Declarations
Authors’ Affiliations
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