Quantization-Based Digital Audio Watermarking in Discrete Fourier Transform Domain

Abstract

<p class="Abstract" style="margin-bottom: 0.0001pt;"><span lang="EN-US">To explore the principle and performances of Q</span><span lang="EN-US">uantization Index Modulation</span><span lang="EN-US"> (QIM) </span><span lang="EN-US">watermarking algorithm</span><span lang="EN-US">, the </span><span lang="EN-US">realization schemes of </span><span lang="EN-US">this</span><span lang="EN-US"> algorithm</span><span lang="EN-US"> in D</span><span lang="EN-US">iscrete Fourier </span><span lang="EN-US">T</span><span lang="EN-US">ransform</span><span lang="EN-US"> (DFT)</span><span lang="EN-US"> </span><span lang="EN-US">d</span><span lang="EN-US">omain</span><span lang="EN-US"> are </span><span lang="EN-US">classified into </span><span lang="EN-US">three</span><span lang="EN-US"> types</span><span lang="EN-US"> according to the </span><span lang="EN-US">differences of quantizer parameters</span><span lang="EN-US">, and then the illustrative diagrams of these schemes are given, so the properties of transparency, robustness and capacity of each scheme are concluded. The scheme with best distortion-robustness trade-off is found on that basis. The more efficient embedding and extracting formulas of this scheme are given, and the performances are researched by theoretical analysis</span><span lang="EN-US"> rather than </span><span lang="EN-US">computer simulation. The conclusions show that the transparency is in proportion to the square of quantization step and small DFT coefficient leads to poor transparency. The robustness to Additive White Gaussian Noise (AWGN) only relates to the quantization step and the algorithm is susceptible to amplitude scale and resampling attacks. The possible capacity is <em>F</em>_o/2<span style="letter-spacing: -0.05pt;"> bits per second, which can satisfy various watermarking applications. To improve the robustness, an adaptive quantization algorithm based on masking properties of the Human Auditory System (HAS) is proposed at the end of this paper, and the algorithm is evaluated in the analog channel environment and its robustness is proved by the results.</span></span></p

Secured data transmission through audio signal (SDTAS)

“…The phase and magnitude of the transform domain coefficients are modified to embed the secret message. Popular transforms include Discrete Fourier Transform (DFT) [10,11,12], Discrete Cosine Transform (DCT) [13] and Discrete Wavelet Transform (DWT) [14].…”

Digital Audio Watermarking Using Psychoacoustic Model and CDMA Modulation

“…The modulated signal is put spectrally in shape by a ( ) H f filter, this filtering has for objective to adapt the spectral density of power of v to a doorstep of masking. The watermark signal is finally got by temporal summons between the audio signal ( ) x n and the signal put in ( ) t n shape [4].The audio signal faces two shapes of disruption; the first is an operation of compression -reconstruction, achieved by a MPEG coder. The second is a desynchronized operation achieved by the transmission via an analogical line between two PCs.…”

“…The watermarking reveals the possibility to use the audio signal as a support of communication. In order to tackle the problem of unauthorized copy and distribution of digital audio data, watermarking systems are employed to insert copyright data into the audio stream [4].…”

Performance Analysis of Watermarking on Indian Classical Songs using Lifting Wavelet Transform and Singular Value Decomposition

“…Thus, image encryption using n times Arnold transforms can be written as [19] (3) where k = 1, 2, … , n, and .…”

“…For an N×N image the Arnold transform, is given as [18][19] ( 1) where (x,y) and (x',y') express the pixel co-ordinates of the original and encrypted images, respectively and is the two dimensional Arnold transform matrix.…”