22-05-2012, 03:44 PM
An adaptive audio watermarking based on the singular value
decomposition in the wavelet domain
Digital Signal Processing.pdf (Size: 301.63 KB / Downloads: 54)
Introduction
Recent advances in Internet and digital multimedia technology have allowed transmission and distribution of digital
multimedia (audio, image and video) easily and efficiently to distant places. However, this convenience allows unauthorized
copying and distribution of multimedia data. Copyright protection of digital data has become an important issue. Digital
watermarking [1] technology has received great deal of attention to solve this problem. Digital watermarking is a process
of embedding watermark data into the audio signal. This embedded data can later be detected or extracted from the audio
signal for various applications. There are several applications of audio watermarking including copyright protection, copy
protection, content authentication, fingerprinting and broadcast monitoring.
In general, an effective audio watermarking [2] scheme must satisfy the following basic requirements: (i) Imperceptibility:
The quality of the audio should be retained after adding the watermark. Imperceptibility can be evaluated using both
objective and subjective measures. According to IFPI (International Federation of the Phonographic Industry) recommendations,
a watermarked audio signal should maintain more than 20 dB SNR. (ii) Security: Watermarked signals should not
reveal any clues about the watermarks in them. Also, the security of the watermarking procedure must depend on secret
keys, but not on the secrecy of the watermarking algorithm. (iii) Robustness: Ability to extract a watermark from a watermarked
audio signal after various signal processing attacks. (iv) Payload: The amount of data that can be embedded into the
host audio signal without losing imperceptibility. For audio signals, data payload refers to the number of watermark data
bits that may be reliably embedded within a host signal per unit of time, usually measured using bits per second (bps).
There should be more than 20 bps data payload.
Synchronization code
Only a few audio watermarking algorithms based on synchronization code have been proposed in the literature [8–10].
De-synchronization attacks (watermark is present but cannot be detected because of a loss of synchronization) pose a serious
problem to any watermarking scheme, especially during audio watermarking. Some attacks, such as cropping, shifting
and MP3 compression (some MP3 encoders unintentionally add around 1000 samples), which change the length of the
audio signal, usually lead to a failure to extract the watermark. So the correct position of the watermark must be identified
before extraction. This problem can be solved by concatenating a synchronization code and watermark bits to form
a binary sequence as shown in Fig. 1. We have embedded the synchronization code in front of the watermark to locate
the position where the watermark is embedded. The detection of the synchronization code is based on the standard frame
synchronization technology.
Watermark embedding algorithm
The block diagram of our watermark embedding algorithm is SVD is an effective numerical analysis
tool used to analyze matrices. In SVD transformation, every real matrix is decomposed into a product of three matrices.
Let A = {Ai j}p×q be an arbitrary matrix with SVD of the form A = U SV T , where U and V are orthogonal p × p and q ×q
matrices, respectively, and S is a p × q diagonal matrix with nonnegative elements. The elements λ
the singular values (SVs) of the matrix A, and u is the rank of the matrix A. The SVD has some interesting properties:
(i) The sizes of the matrices from SVD transformation are not fixed, and the matrices need not be square.
Experimental results and discussions
We have performed extensive simulations using MATLAB 7.1 on different audio signals including classical, country, blues,
jazz and pop music. Each music is a 16-bit mono audio signal in the WAVE format sampled at 44 100 Hz. A plot of a short
portion of the jazz audio signal and its watermarked version is shown in Fig. 3. The embedded watermark is the binary
logo image of size M × M = 32 × 32 = 1024 bits, shown in Fig. 4. We use a 16-bit barker code 1111100110101110 as the
synchronization code and an audio segment length of n = 484 samples for embedding the synchronization code. In our
experiment, we have set 2-D matrix size u × u = 22 ×22. We have set the weight parameters Smean = 0.1 and Sstd = 0.6,
the minimum quantization parameter m = 0.5 or 0.6 or 0.7 and the maximum quantization parameter M = 0.9. All these
parameters have been chosen so as to achieve a good compromise between the contending requirements of imperceptibility,
robustness and payload. The threshold e defined in Section 4.1 is set as 0.96.