Watermark Mobile Application

Group Members:

• Ceren Bülbül - 20160808052
• Banu Duman - 20150807032

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1. General Definition Of Digital Watermarking
2. History Of Watermarking
3. Watermark Types
4. Digital Watermarking Process (Life Cycle)
5. Classification Watermarking Techniques
   • Based on Human Perceptron
     • Visible
     • Invisible
     • Robust
     • Fragile
   • Based on Domain
     • Frequency Domain
     • Grayscale
     • Spatial Domain
     • BitStream Domain
     • Bluring
6. Implementation
7. Demo
   • Demo on Web
   • Demo on Android
   • Demo on IOS
8. RESOURCES

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General Definition Of Digital Watermarking

A watermark is a logo that stands for watermark and prevents copying when placed on an image or image. Sometimes it can be used not as a logo, but as a special text. When using Watermark, you can block the copy or see the original author even if there is a copy. Sometimes the site logo and sometimes different names are placed on the image or in a blocking corner to create this purpose.

The picture above is an example of a watermark. He embedded the logo (watermark) into the picture, making it less obvious. Thus, stealing the logo has become more difficult.

Watermark is a feature that is generally valid for transactions on the internet. Therefore, if you want to watermark any image you want to protect, you can easily do this from your computer. Watermark can be created very easily with any photo editing program. One of these programs is the "Watermark" application that we have implemented.

History Of Watermarking

The term "Digital Watermark" was coined in 1992 by Andrew Tirkel and Charles Osborne.

Watermarks are identification marks produced during the papermaking process. The first watermarks appeared in Italy in the 13th century, but their use quickly spread throughout Europe. Watermarks continue to be used today as manufacturer marks and to prevent counterfeiting.

Watermark Types

• Text: The manuscripts must be protected and not stolen. Therefore, it is one of the watermark types. In the picture below, the watermark technique is used to prevent the text from being stolen. The image below is an example of a watermark made for the Text.

• Image: Images can be made more secure by using watermark techniques so that they are not copied or stolen. Some watermark techniques have been applied to the pictures in the image below and the pictures have been made reliable.

• Audio: Copying sounds can cause many problems nowadays. Therefore, watermark techniques are used to prevent the sounds from being copied. The following example shows how the watermark technique is applied to a sound.

• Video: Copying videos can cause many problems nowadays. Therefore, watermark techniques are used to prevent the videos from being copied.

Digital Watermarking Process (Life Cycle)

• Embed : In this part, the digital signal is embedded with the digital watermark. In Embedding, an algorithm accepts the host and the data to be embedded and produces a watermark signal.

• Attack : İletilen medyanın değiştirildiği an, bir tehdit haline gelir ve filigran sistemine saldırı olarak adlandırılır. Filigranlı dijital sinyal iletilir veya saklanır, genellikle başka bir kişiye iletilir. Bu kişi değişiklik yaparsa buna saldırı denir.

• Detection : The detection of the watermark from the noisy signal which might have altered media (JPEG compression, rotation, cropping, and adding noise) another called is Protection or Extraction. Extraction/Detection is an algorithm that is applied to the attacked signal to attempt to extract the watermark from it. If the signal was unmodified during the transmission, then the watermark still is present and it may be extracted.

Classification Watermarking Techniques

There are some techniques applied to make watermarks. These techniques are schematized as follows.

Based on Human Perceptron

• Visible

A watermark logo can be viewed as the property of the ownership. In the example shown below, the logo is visible. However, its opacity has been lowered.

• Invisible

These watermarks are embedded in the media and use steganography technique. They are not visible by naked eyes. Distortion of embedded is too small to be noticed. A watermark can be extracted by authorized extraction software. The image below is an example made using invisible watermark techniques.

• Fragile

In the Fragile Watermarks, watermark fails to be detected from the watermarked image after slight modification. These watermarks are destroyed by data manipulation. There must be a system which can detect all changes in the data if fragile watermarks are to be used.

• Robust

In the Robust watermark, the watermark resists different types of attack. Robust watermarks may be used in copy protection applications to carry copy and no access control information. In the example below, a watermark was made using the Robust feature.

Nowadays,there is an ever growing interest in copyright protection of multimedia content, thus digital watermarking techniques are widely practiced.

Based on Domain

• Frequency Domain

Adding or changing components of digital images can be transferred to other value domains. Embedding watermark schemes in frequency coefficients will produce higher robustness than embedding watermark schemes in the spatial domain. A watermarking scheme that uses frequency-based should embed a watermark in low-frequency order or medium frequencies. Embedding a watermark in a low frequency can produce a higher quality degradation of the watermarked image. Embedding a watermark in a high frequency makes it less robust and it is a higher probability of being lost when the watermarked images are compressed. Discrete Cosine Transform (DCT) and Waveland Transform can be given examples. In the picture below, a watermark was made using the Waveland Transform method. As can be seen from the picture, the image-decompositon was used while using the Waveland Transform method.

• Grayscale

DCT algorithm helped us while converting the grayscale image to black and white. DCT is often used in signal and image processing, especially for lossy data compression, most of the signal information tends to be concentrated in a few low-frequency components of the DCT.DCT is used in JPEG image compression, MJPEG, MPEG, DV, and Theora video compression. There, the two-dimensional DCT of NxN blocks are computed and the results are quantized and entropy coded.

• Spatial Domain

Digital watermarking manipulate the pixel in randomly selected values for adding watermark information. For example Least significant bitstream. Randomly selected image data are dithered by a small amount according to a predefined algorithm, whose complexity may vary in practical systems. The algorithm defines the intensity and the position of the watermark on the original image. In the picture below, watermark was made using the Spatial Domain method. The logo is embedded in the middle of the picture and inside the picture.

• BitStream Domain

Bit-stream watermarking is generally known within the art. F. Scale factors are bit-stream signal parameters used in the sub-band domain for optimizing the coding efficiency. However, the prior art does describe a system that works with additive watermarks only.

• Bluring

When a picture has a logo on it, when we want to use the picture, we can make the logo invisible using watermark techniques. When an image has a logo on it, when we "blur" that logo, the visibility of the logo will be gone. Thus, we have separated the picture and the logo with the watermark technique. We can do the same with trimming. When we crop the part of the logo, the image and the logo will be separated.

Implementation

• Visible Function: When we put the logo on a picture, we have made a watermark by reducing the visibility (transparency) of the logo.

  <Image
     source={{ uri: mark }}
     blurRadius={getBlur}
     style={{
     position: 'absolute',
     width: getImageWidth,
     height: getImaheHeight,
     alignSelf: "center",
     resizeMode: 'contain',
     position: 'absolute',
     opacity: getOpacity,
  }} />
  

The "opacity: getOpacity" line in the above code reduces the visibility of the logo. The user can make a watermark by providing the transparency of the logo as desired. The following example shows how it looks in our app. The first figure has a logo and a picture. In the second figure, the opacity of the logo has been reduced. In the third figure, the opacity of the logo, which is the logo text, has been reduced.

• Frekans Domain (Grayscale) Function: We tried to implement the "DCT" algorithm, one of the frequency domain algorithms. The code given below contains the "DCT" algorithm. While implementing the code, we applied the formula given below.

dct[i][j] = ci * cj (sum(k=0 to m-1) sum(l=0 to n-1) matrix[k][l] * cos((2k+1) ipi/2m) * cos((2l+1) jpi/2n) where ci= 1/sqrt(m) if i=0 else ci= sqrt(2)/sqrt(m) and similarly, cj= 1/sqrt(n) if j=0 else cj= sqrt(2)/sqrt(n) and we have to apply this formula to all the value, i.e., from i=0 to m-1 and j=0 to n-1 Here, sum(k=0 to m-1) denotes summation of values from k=0 to k=m-1.

    for (i = 0; i < m; i++) {
            for (j = 0; j < n; j++) {
                if (i == 0)
                    ci = 1 / Math.sqrt(m);
                else
                    ci = Math.sqrt(2) / Math.sqrt(m);
                if (j == 0)
                    cj = 1 / Math.sqrt(n);
                else
                    cj = Math.sqrt(2) / Math.sqrt(n);

                sum = 0;
                for (k = 0; k < m; k++) {
                    for (l = 0; l < n; l++) {
                        dct1 = matrix[k][l] *
                            Math.cos((2 * k + 1) * i * pi / (2 * m)) *
                            Math.cos((2 * l + 1) * j * pi / (2 * n));
                        sum = sum + dct1;
                    }
                }
                setDCT(ci * cj * sum)
                myMap.set(count,ci * cj * sum)
                count = count+1;
            }
        }

The code given above does not run the Frequency Domain algorithm as we would like. That's why we used the "Grayscale" algorithm instead. We watermarked with the "Grayscale" algorithm. Using the code below, we ensure that the logo or image is "grayscale".

backgroundColor: 'gray'

As shown in the figure above, our application can turn the given logo and image into "grayscale". In this way, it can watermark. In the first figure, there is a logo and a picture. In the second figure, there is a logo and picture turned into grayscale. In the third figure, there is an example made with our application but with another picture and logo entered.

• Spatial Domain Function: While implementing the Spatial Domain algorithm, the entered logo is enlarged and brought to the background and centered. We implemented the Spatial Domain algorithm as follows. In the code above, the logo is set to be directly behind the image using the "getImageWidth" and "getImaheHeight" values.

  <Image
    source={{ uri: mark }}
    style={{
      position: 'absolute',
      width: getImageWidth,
      height: getImaheHeight,
      alignSelf: "center",
      resizeMode: 'contain',
      position: 'absolute',
      opacity: getOpacity,
  }} />
  

The picture below shows how the "Spatial Domain" algorithm works in our application. In the first figure, the entered logo and picture are shown. In the second figure, the size of the logo has been changed, made transparent and centered. In the third figure, the same procedure was performed with different samples. It is more clearly understood in the third figure.

• BitStream Domain (Bluring) Function: Bit Stream algorithm has been tried to be implemented. However, since the desired result could not be given exactly, a watermark method was used that blurs the "logo". The code below shows how the blur function is implemented. We implemented bluring using "blurRadius" attribute.

  <Image
    source={{ uri: mark }}
    blurRadius={getBlur} />
  

The figures below show how the "bluring watermark" function works in our application. The picture and logo given in the first figure are shown. In the second figure, the logo and picture are shown as blurred. In the third figure, the bluring function is applied in another example. The third example shows it more clearly.

Demo

Demo: https://youtu.be/tIw9950hrc4

Demo on Web

watermarker_web.mp4

Demo on Android

watermarker_mobile.mp4

Demo on IOS

watermarker_ios2.mp4

RESOURCES

1. Digital watermarking
2. IMAGE WATERMARKING
3. Digital Watermarking Classification : A Survey
4. Digital watermarking
5. Digital Watermarking and its Types
6. Digital watermarking techniques for image security
7. Visible Watermarking Technique Based on Human Visual System for Single Sensor Digital Cameras
8. An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection
9. A robust and secure watermarking scheme based on singular values replacement
10. GRAYSCALE IMAGE WATERMARK DETECTION