Such messages usually carry copyright information of the file. Digital watermarking takes its name from watermarking of paper or money. But the main difference between them is that digital watermarks are supposed to be invisible or at least not changing the perception of original file, unlike paper watermarks, which are supposed to be somewhat visible.
Speaking of digital image watermarking, we can divide watermarks into two main groups — visible and invisible watermarks. A visible watermark is a visible semi-transparent text or image overlaid on the original image.
Visible watermarks are more robust against image transformation especially if you use a semi-transparent watermark placed over whole image. Only electronic devices or specialized software can extract the hidden information to identify the copyright owner. Invisible watermarks are used to mark a specialized digital content text, images or even audio content to prove its authenticity. A visible image watermark can be added using a graphics editor like free MS Paint. Please, look at the examples of visible watermarks added with Bytescout Watermarking Pro software. Find more information about Bytescout Watermarking Pro software here.
Check our Watermarking freeware here. I use ByteScout Watermarking Pro very often, because I have a photo blog where it is important that the pictures are not copied that they can use it as their owns. Digital watermark types and what is the digitial watermark? Quick information and samples. Digital watermark types and general information about digital watermarking for images.
Tutorials: How to add embossed text to the picture using Bytescout Watermarking Pro. Digital watermarking hides watermarks in digital media images, voice, video, etc. If there is no robustness requirement, the processing of watermark and information camouflage technology is completely consistent . In most cases, we want to add information that is invisible or invisible; in some specific situations where visible digital watermarks are used, the copyright protection mark is not required to be hidden, and it is desirable that the attacker does not destroy the quality of the data itself.
The watermark cannot be removed. Therefore, we can summarize functions of digital watermarking technology into two aspects. In this paper, the two-dimensional discrete cosine transform is used to realize the embedding and extracting of the digital watermarking algorithm.
The forward DCT is used to convert the image block information into the coefficient frequency domain matrix, and then the inverse DCT is used to transform the watermarked coefficient matrix into the image block. This paper begins with a detailed introduction to the basic knowledge related to digital watermarking and classical algorithms, and briefly describes the DCT transforms that will be used in this paper.
Then, the detector could be programmed to select either a default luminance converter, or one tuned to a specific type of sensor. The robust type watermark can be embedded to images via specific techniques, and the existence thereof can be detected even after image processing, such that the copyright can be protected. Xiangtao Li, Editor. Based on the probability distributions of marked and unmarked images, it determines the likelihood that a given detection value for an input image originates from a marked and unmarked image. For constructing geometric invariant watermarking, four mainstream schemes are introduced by literature reviews on watermarking algorithms robust to the geometrical distortions. Ruan QQ.
After that, the traditional algorithms are improved accordingly. According to the algorithm, in the intermediate frequency the watermark is embedded in the coefficient to realize the adaptive embedding of the watermark. At the end of the paper, the performance of the watermarking system is analyzed and evaluated. After the embedding and extraction, the two methods of attack and detection are performed.
If the watermarked image cannot be seen and the watermark is still identifiable, the watermark is proved to be robust and imperceptible. Digital Watermarking technology hides some information that has special meanings into digital media information such as text files, digital audio, video, images, etc.
Digital watermarking and information embedding using dither modulation. Abstract: A variety of related applications have emerged that require the design of . A digital watermarking method is referred to as robust, but also to have a low information capacity due to host interference.
And the change of size does not affect the use value. When the watermark extraction detected, the hidden information cannot be lost. In order to make digital watermarks a trusted application system for digital product copyright protection and integrity identification, embedded information entered into digital products must have the following basic characteristics:.
After such a processing operation, the watermark must be able to be distinguished and recognized. Only in this way can the function of the watermark system be reflected in the event of a dispute. It is mainly applied to the image, there are also applications in video and audio, and audible watermarks in audio. When viewing an image or video, the watermark is imperceptible and largely retains the value of the digital work.
But when there are problems like copyright disputes that are difficult to solve, as long as the watermark can be extracted, the complicated problem becomes very simple. Its purpose is to protect the digital copyright after it has been processed filtered, noisy, replaced, compressed, etc. Its advantage is strong robustness. However, due to the dispersion of network propagation information, the practical value of this watermark in the application process is not broad. The aspect is very simple, but the robustness is not very strong.
At this time, the watermark already exists in any place of the carrier file. When the spatial domain algorithm is applied to the digital watermarking technology, when the digital artwork embedded in the watermark is subjected to some common attacks, the watermark signal embedded therein is easily lost. In view of this situation, the researchers have proposed the idea of embedding the watermark in the transform domain. From the time domain to the transform domain, mainly through some mathematical transformations, these mathematical transformations have discrete cosine transform DCT and discrete fourier transform DFT.
At this time, some frequency domain coefficients of the image are embedded in the watermark information. For this change, it means that the signal of the watermark information may be released to any place in the entire image space, and then the transformed frequency domain image is transformed into a time domain watermarked carrier.
After passing this series of transformations, the watermark signal will not be removed so easily.
Adding digital watermark principle in frequency domain: Adding digital watermark in frequency domain means transforming image into frequency domain by some transform such as Fourier transform, discrete cosine transform and wavelet transform, adding the watermark to the image in frequency domain and then transform the image into a spatial domain by inverse transform. Compared with airspace means, the frequency domain means is more occult and more resistant to attack. The watermark information is trapped in the low frequency coefficient, which is sensitive to the human eye, which will cause a significant drop in the quality of the carrier image; embedding the high frequency coefficient will cause serious damage to the watermark information embedded therein.
Therefore, based on the DCT domain watermark embedding intermediate frequency coefficients, a good compromise between watermark transparency and robustness is achieved.
Then, the method of exchanging intermediate frequency coefficients is used, which is commonly used in traditional algorithms to improve the embedding of binary watermarks, so that the larger value of the system is larger after the system is exchanged, and the smaller value is smaller, thereby obtaining stronger robustness. The discrete cosine transform is equivalent to a discrete Fourier transform of approximately twice the length.
This discrete Fourier transform is performed on a real function, because the Fourier transform of a real function is still a real function, in some variants, it need to move the input or output position by half a unit . It is a special case in the image processing that is widely used in the Fourier transform. The expanded function is a real function, and then discretized, that is a discrete cosine transform.
There are two basic methods for modifying the coefficients, as shown in formula 5 and formula 6 :. Modify the coefficient formula 7 :. Figure 1. Watermark embedding block diagram. The so-called attack on the watermark refers to the destruction of the watermark, including smearing, shearing, scaling, rotation, compression, noise addition, filtering, and the like. Digital blind watermarking is not only about agility, but also defensive.
The imperceptibility and robustness of digital blind watermarks are mutually exclusive . Image noise is often viewed as a multidimensional random process, so the method of describing noise can be borrowed from the description of a random process. White noise refers to the noise energy contained in a band of equal bandwidth over a wide frequency range. It is a random signal or stochastic process with a constant power spectral density. In other words, the power of this signal is the same in each frequency band.
This signal is therefore also referred to as white noise  .
Gaussian filtering is a linear smoothing filter that is suitable for eliminating Gaussian noise and is widely used in the noise reduction process of image processing. Figure 2. Watermark extraction block diagram. Low-pass filtering is used to smooth the image. The goal of the low-pass filter is to reduce the rate of change of the image. For example, replace each pixel with the mean of the pixels around the pixel. This makes it possible to smooth and replace areas where the intensity changes significantly  .
The difference between low-pass filtering and Gaussian filtering is that in low-pass filtering, the weight of each pixel in the filter is the same, that is, the filter is linear. The weight of the pixels in the Gaussian filter is proportional to the distance from the center pixel. It can be classified into stable robustness and performance robustness . In the specific context of this article, robustness refers to the ability to detect watermarks from watermarked images after noise attacks, low-pass high-pass filtering, geometric distortion, etc.
Robustness for watermarks. It is a very important feature. Of course, the larger the value, the better. Fair algorithm comparison and performance evaluation between different digital watermarking systems are of great significance for the standardization of digital watermarks and the practical application of watermarks .