Hidden Bits In Wavelet Domain
A possible domain for watermark embedding is that of the wavelet domain. The Discrete Wavelet Transform separates an image into a lower resolution approximation image as well as horizontal, vertical and diagonal detail components. The process can then be repeated to computes multiple “scale” wavelet decomposition. One of the many advantages over the wavelet transform is that that it is believed to more accurately model aspects of the Human Visual System (HVS) as compared to the FFT or DCT. This allows us to use higher energy watermarks in regions that the HVS is known to be less sensitive to, such as the high resolution detail bands. Embedding watermarks in these regions allow us to increase the robustness of our watermark, at little to no additional impact on image quality. One of the most straightforward techniques is the embedding of a CDMA sequence in the detail bands. The wavelet domain as well proved to be highly resistant to both compression and noise, with minimal amounts of visual degradation. This is all the more impressive when one considers that the wavelet technique described here is one of the most primitive currently known. More sophisticated wavelet-domain techniques will almost certainly improve on both of these, and hopefully lower it’s computational requirements. The wavelet domain may be one of the most promising domains for digital watermarking yet found.
We have developed a new scheme for the embedding of watermark sequence with high capacity, using a multilevel approach for coefficients selection.
Index Terms: Matlab, source, code, wavelet, watermarking, capacity, human, visual, system, multilevel.
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Figure 1. Visible watermark |
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A simple and effective source code for High-Capacity Wavelet Based Watermarking. |
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Demo code (protected P-files) available for performance evaluation. Matlab Wavelet Toolbox is required. |
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Major features
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1.0
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2009.06.11 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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High-Capacity Wavelet Based Watermarking. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 100 EUROS (less than 140 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of High-Capacity Wavelet Based Watermarking. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Wavelet Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 3% [?]
Copyright Protection of Digital Audio Data
The outstanding progress of digital technology has increased the ease with which digital data is reproduced and retransmitted. However, since the advantages of such a progress are broadly available, they offer equally increasing potential to both legal and unauthorized data manipulation. Consequently, the necessity arises for copyright protection of digital products against unauthorized recording attempts, knows as data piracy. Current research in image, audio and video copyright protection exploits the fact that the human visual and audio perception cannot detect slight changes in certain temporal or frequency domains of the image and the audio signal, respectively. This property is called masking, according to which a faint but perceptible signal becomes non-perceptible in the presence of another one under certain conditions. Most research methods consider a watermark signal produced in a unique way by a function of one or more input keys. These keys can be both owner and signal dependent and generate a signal which is embedded on the original one. The embedding signal is known as a watermark or copyright label. Temporal and frequency characteristics of the original signal should be taken into account in the watermark casting process to reduce perceptible distortions in the watermarked signal. Each individual that produces or possesses digital data owns a unique key that identifies its legal possession and is required for the watermark detection. Besides copyright purposes, a watermark serves authentication purposes, as well.
A watermark has to be statistically undetectable by others to prevent the efforts of its unauthorized removal. This condition is fulfilled if the potential number of keys that produce distinct watermarks is large enough to ensure statistical safety. The detection scheme should be as statistically reliable as possible. False rejection or acceptance of the existence of the watermark should be minimal. Finally, a watermark has to be robust to signal manipulation and impossible to be removed without significant alteration of the signal. In other words, a pirate should have to destroy the audio signal before he accomplishes to destroy the watermark. The robustness should extend to common signal processing operations, such as filtering, compression, resampling, requantization, cropping, noise, D/A conversion.
Index Terms: Matlab, source, code, watermarking, watermark, detection, embedding, audio, copyright, protection.
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Figure 1. Copyright protection |
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A simple and effective source code for Digital Audio Watermarking. |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox is required.
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Major features
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1.0
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2008.04.19
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Digital Audio Watermarking – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 90 EUROS (less than 126 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Digital Audio Watermarking. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Signal Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 2% [?]
LSB Based Steganography
Steganography is an ancient art of conveying messages in a secret way that only the receiver knows the existence of message. So, a fundamental requirement for a stegano- graphic method is imperceptibility; this means that the embedded messages should not be discernible to the human eye. There are two other requirements, one is to maximize the embedding capacity, and the other is security. The least-significant bit (LSB) insertion method is the most common and easiest method for embedding messages in an image. However, how to decide on the maximal embedding capacity for each pixel is still an open issue. An image steganographic model is proposed that is based on variable-sized LSB insertion to maximise the embedding capacity while maintaining the image fidelity. For each pixel of a gray-scale image, at least 4 bits can be used for messages embedding. First, according to contrast and luminance characteristics, the capacity evaluation is provided to estimate the maximum embedding capacity of each pixel. Then, the minimum-error replacement method is adapted to find a gray-scale as close to the original one as possible.
Index Terms: Matlab, source, code, LSB, least, significant, bit, steganography.
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Figure 1. Bitstream |
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A simple and effective source code for High Capacity Image Steganographic Model. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
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1.0
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2009.06.20
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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High Capacity Image Steganographic Model. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 100 EUROS (less than 140 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of High Capacity Image Steganographic Model. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 8% [?]
Fast and Accurate Face Identification Using Overlapping DCT
In the JPEG image compression algorithm, the input image is divided into 8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each block. The DCT coefficients are then quantized, coded, and transmitted. The JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients, computes the inverse two-dimensional DCT of each block, and then puts the blocks back together into a single image. For typical images, many of the DCT coefficients have values close to zero; these coefficients can be discarded without seriously affecting the quality of the reconstructed image. Such algorithm results particularly robust also for face identification. Moreover the 2D DCT operator can be applied to overlapping data.
The extracted feature vectors are used as input to a simple nearest neighbor algorithm. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors. k is a positive integer, typically small. If k = 1, then the object is simply assigned to the class of its nearest neighbor. In binary (two class) classification problems, it is helpful to choose k to be an odd number as this avoids difficulties with tied votes. The same method can be used for regression, by simply assigning the property value for the object to be the average of the values of its k nearest neighbors. It can be useful to weight the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the correct classification (or, in the case of regression, the value of the property) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required. In order to identify neighbors, the objects are represented by position vectors in a multidimensional feature space. It is usual to use the Euclidean distance, though other distance measures, such as the Manhattan distance could in principle be used instead. The k-nearest neighbor algorithm is sensitive to the local structure of the data.
The code has been tested with AT&T database achieving an excellent recognition rate of 99.20% (40 classes, 5 training images and 5 test images for each class, hence there are 200 training images and 200 test images in total randomly selected and no overlap exists between the training and test images).
Index Terms: Matlab, source, code, face recognition, face matching, face verification, dct, k-nearest neighbor algorithm, knn, discrete cosine transform.
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Figure 1. Example of k-NN classification |
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A simple and effective source code for Face Recognition. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required. |
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1.0
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2007.10.20 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Face Recognition Based On Overlapping DCT – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 250 EUROS (less than 350 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Face Recognition Based On Overlapping DCT. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab Release 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 3% [?]
A Digital Image Copyright Protection Scheme Based on Visual Cryptography
A simple watermarking method for color images is proposed. The proposed method is based on watermark embedding for the histograms of the HSV planes using visual cryptography watermarking. The method has been proved to be robust for various image processing operations such as filtering, compression, additive noise, and various geometrical attacks such as rotation, scaling, cropping, flipping, and shearing.
The watermark method is an excellent technique to protect the copyright ownership of a digital image. The proposed watermark method is built up on the concept of visual cryptography. According to the proposed method, the watermark pattern does not have to be embedded into the original image directly, which makes it harder to detect or recover from the marked image in an illegal way. It can be retrieved from the marked image without making comparison with the original image. The notary also can off-line adjudge the ownership of the suspect image by this method. The watermark pattern can be any significant black/white image that can be used to typify the owner. Experimental results show that the watermark pattern in the marked image has good transparency and robustness. By the proposed method, all the pixels of the marked image are equal to the original image.
Index Terms: Matlab, source, code, histogram, HSV, visual, cryptography, watermark, hue, saturation, value.
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Figure 1. Hue-Saturation-Value color space |
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A simple and effective source code for HSV Image Watermarking Scheme Based on Visual Cryptography. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
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Major features
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1.0
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2009.06.11 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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HSV Image Watermarking Scheme Based on Visual Cryptography. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 200 EUROS (less than 280 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of HSV Image Watermarking Scheme Based on Visual Cryptography. Alternatively, you can bestow using our banking coordinates:
|
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 3% [?]
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