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What are the technique of Frequency domain watermarking?
The main goals of frequency domain watermarking is to embed the watermarks in the spectral coefficients of the image. The most generally used transforms are the Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Discrete Wavelet Transform (DWT).
The main reason for watermarking in the frequency domain is that the nature of the human visual system (HVS) are better acquired by the spectral coefficients.
Discrete cosine transforms (DCT) − DCT like a Fourier Transform. It can define data in terms of frequency space instead of an amplitude space. This is beneficial because that corresponds more to the method humans perceive light, therefore that the part that are not perceived can be recognized and thrown away.
DCT based watermarking techniques are powerful compared to spatial domain techniques. Such algorithms are powerful against simple image processing operations such as low pass filtering, brightness and contrast adjustment, blurring etc.
However, they are complex to perform and are computationally more expensive. At the similar time they are weak against geometric attacks such as rotation, scaling, cropping etc.
DCT domain watermarking can be defined into Global DCT watermarking and Block based DCT watermarking. Embedding in the perceptually essential part of the image has its own advantages because most compression schemes eliminate the perceptually insignificant part of the image.
There are the following steps in DCT which are as follows −
It can be used to segment the image into non-overlapping blocks of 8x8.
It can apply forward DCT to each of these blocks.
It can apply some block selection criteria (e.g. HVS).
It can apply coefficient selection criteria (e.g. highest).
It can be embed watermark by modifying the selected coefficients.
It can be apply inverse DCT transform on each block.
Discrete wavelet transforms (DWT) − Wavelet Transform is a modern approach frequently utilized in digital image processing, compression, watermarking etc. The transforms are based on small waves, called wavelet, of changing frequency and limited period.
The wavelet transform disappear the image into three spatial directions, such as horizontal, vertical and diagonal. Thus wavelets reflect the anisotropic features of HVS more precisely. Magnitude of DWT coefficients is bigger in the lowest bands (LL) at every level of decomposition and is smaller for other bands (HH, LH, and HL).
The Discrete Wavelet Transform (DWT) is generally used in a broad method of signal processing applications, including in audio and video compression, elimination of noise in audio, and the simulation of wireless antenna distribution.
Discrete Fourier transform (DFT) − It can change a continuous function into its frequency elements. It has robustness against geometric attacks such as rotation, scaling, cropping, translation etc.
DFT shows translation invariance. Spatial shifts in the image affects the phase description of the image but not the magnitude description, or circular change in the spatial domain don't influence the magnitude of the Fourier transform.
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