Adding Watermark to Images in Computer Vision

Introduction

We can perform several crucial tasks using the effective Python package known as OpenCV. This library's capabilities include tracking camera movements, extracting 3D models of any item, making image filters, and adding watermarks. The cv2.addWeighted() function from OpenCV will add a watermark to a picture.

Syntax

cv2.addWeighted(src1, alpha, src2, beta,y)

Parameters

src1: The first input image. alpha: Weight of the first input image. src2: The second input image. beta: Weight of the second input image. gamma: Scalar value added to each pixel after blending.

Lets implement this function further for different type of watermark on images along with different techniques in implementation.

Types of Watermarks

Nevertheless, before you begin watermarking your images, you need to know the many types of watermarks and when to use them.

Visible Watermarks

As implied by the name., viewers may easily notice a visible watermark. In essence, it's the original photos with the semi-transparent identifiers superimposed. These identifiers, which demonstrate who owns the photographs, can be text, an address, a URL, a logo, a QR code, or anything else. These are semi-transparent, but they still let you see the original pictures.

Visible watermarks work best for advertising and protecting photos from copyright violations. They are not very useful in identifying image piracy. But, with the right software, they may be quickly trimmed, covered with another watermark, or eliminated. Any benefit, however, comes with a cost.

Invisible Watermarks

Hidden or covert watermarks are other names for invisible watermarks. The naked eye can't see these watermarks since, as their name suggests, they are invisible. You'll need specialized steganography software or equipment to view them.

There are several varieties of undetectable watermarks

1. Several of these watermarks resemble visible watermarks, but because they are so transparent, no one can see them with the unaided eye.
2. Some particular pixels have their lowest-order bit reversed, however, this only functions if the image remains unchanged. It's too simple to use, though.
3. Another method for invisible watermarking is spatial watermarking. It uses a particular color band that is apparent when the colors are separated, for example, during printing.

Text-based Watermark

Let's generate a string that contains the text that will serve as the watermark for the image. The font needs to be configured next. There are numerous built-in font styles available in OpenCV. The watermark text will be white due to the (255, 255, 255). Finally, we can add the watermark text to the image using the putText() method.

Output

Image-based Watermarks

Here are some key points about adding image-based watermarks to images using OpenCV:

• Image-based watermarks are commonly used to protect digital images from unauthorized use or distribution.
• OpenCV provides a set of powerful image-processing tools that can be used to add image-based watermarks to images.
• The watermark image should be carefully designed to balance the need for visibility with the desire to minimize interference with the underlying image.
• When adding a watermark to an image, it is important to use techniques that preserve the visual integrity of the original image while still making the watermark visible.
• The process of adding an image-based watermark to an image typically involves loading the original image and watermark image, resizing the watermark to fit the original image, creating a binary mask that defines the location of the watermark, blending the watermark image with the original image using the binary mask, and saving the watermarked image.

Techniques for Adding Watermarks

Digital Watermarking Techniques

The process of inserting side information specifically designated by the information's owner into the source (cover) information is known as watermarking. The purpose of digital watermarking is to conceal the owner's visual information inside the source visual data. Applications involving images and videos frequently use it.

On the other hand, digital watermarking embeds the mark so that users of the source data can hardly tell the difference between the information. Even when modifying the watermarked information strongly, the hidden information cannot be removed despite being detectable.

Several kinds of watermarking exist, such as dual, invisible, and visible. A geographical or modified domain may serve as the foundation for watermarking algorithms. Modifying the pixel values of one or more randomly chosen subsets of images is known as spatial domain watermarking. The raw data is directly loaded into the image pixels. It might be founded on applying various patches or the least significant bit planes. Transform coefficient alteration is the foundation of transform domain watermarking. Several frequently employed transform domain techniques, including DCT or DWT.

Spatial Domain Techniques

Spatial Domain Digital Watermarking a technique for adding watermarked data (side data set by the owner) to the source (cover) image or video in the spatial domain, is known as digital watermarking. The spatial domain defines the image as pixels. A few selected pixels' intensity and colour value are changed as part of the spatial domain watermarking process to embed the watermark. While the spatial domain watermarking is more straightforward and has a higher calculation speed than the transform domain, it is less resistant to attacks. You may easily apply the spatial domain approaches to any image. Least Significant Bit Modification is the most popular watermarking algorithm for spatial domains. This technique modifies the image's selected pixels' least significant bits (LSB). Using a similar technique to additional LSB bits of the container image is feasible.

The container image may include many instances of the watermarked object. Even if attacks cause the majority of the watermarks to be lost, only one remaining is sufficient. This approach is rather straightforward. It can withstand straightforward operations like noise addition and cropping. Nevertheless, lossy compression will render the watermark useless. Additionally, the watermark may be removed with little perceptible harm to the cover object by performing a direct attack that sets all the LSB bits to "1".

The watermark is extracted using an exclusive-or operation utilising the watermark template after the LSB plane has been removed from the watermarked image.

The cover image may be pre-processed to stop the hidden information from being extracted directly from the chroma LSB. The cover image can be preprocessed by being divided into blocks. The relationship between nearby blocks is then changed to encode the embedded watermark.

Frequency Domain Techniques

Frequency domain watermarking's primary objective is to include the watermarks in the image's spectral coefficients. The Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Discrete Wavelet Transform(DWT) are the transforms that are utilized the most frequently. Domain watermarking using the DCT (discrete cosine transform) is resistant to attacks like noise, compression, sharpening, and filtering. Its foundation is the common JPEG compression algorithm, and watermarking can be applied as a parameter. In contrast to spatial domain watermarking, which modifies the image bit plane's pixel LSB, DCT Transform Digital Watermarking alternates the frequency coefficients.

DCT makes it simple to insert watermarking data into an image's frequency bands since it enables an image to be divided into various frequency bands. The intermediate frequency bands are suitable for embedding. It improves resistance to assaults that frequently have the potential to amplify higher frequencies. The most visually appealing portions of the image, which are focused on the lower frequencies, are also avoided by using intermediate frequencies. The DCT transform works by breaking up the original image into 8 by 8 pixel blocks and performing the transform on each block separately. Using a middle-frequency 2-D mask allows the middle-frequency range to be retrieved from the DCT coefficients. A contemporary wavelet transform technique is frequently used in digital image processing, compression, watermarking, etc. The transformations are based on tiny waves with a finite period and varying frequency, known as wavelets.

The wavelet transform divides the image into three spatial directions—vertical, horizontal, and diagonal—. At every level of decomposition, the magnitude of the DWT coefficients is larger in the lowest bands (LL) and smaller for other bands (HH, LH, and HL). Hence, wavelets more accurately capture the anisotropic characteristics of HVS.

The Discrete Wavelet Transform (DWT) is frequently employed in various signal processing tasks, such as audio and video compression, the removal of background noise from audio, and the simulation of wireless antenna placement.

A continuous function can be transformed into its frequency components using the discrete Fourier transform (DFT). It is resistant to geometrical attacks like rotation, scaling, cropping, and translation.

DFT demonstrates translation invariance. The Fourier transform's magnitude is unaffected by spatial shifts in the image; instead, they modify how the image is described in terms of its phase, not its magnitude.

Transform domain techniques

In the transform domain, an image is expressed in terms of frequencies. To convert an image to its frequency representation, we can apply a variety of reversible transforms, including the discrete cosine transform (DCT), discrete wavelet transform (DWT), discrete Fourier transform (DFT), contourlet transform (CT), and singular value decomposition (SVD). Each morph has unique qualities and depicts the image in various ways.

DCT Watermarking: This technique involves altering the DCT coefficients to incorporate the watermark into the image's DCT domain.

Wavelet Transform Watermarking: This method embeds the watermark in the high-frequency subbands after decomposing the image into various frequency subbands using the wavelet transform.

Singular Value Decomposition (SVD) Watermarking: This method embeds the watermark in the singular values after decomposing the image into its singular value matrix using SVD.

Applications of Watermarking in Computer Vision

1.Intellectual property protection: Watermarks can protect intellectual property by uniquely identifying an image. This can help to prevent the image from being used or copied without permission.

2.Security of digital images: Watermarks can be used as a precaution to prevent illegal access to or usage of digital images. For example, a watermark can be added to indicate that an image is protected, making it more difficult for others to use the image without permission.

3. Watermarking digital images: Watermarks can be used to verify the origin of a digital image. A watermark, for example, might be placed on an image to show that it was created by a specific photographer or organization, making it easier to authenticate the image's legitimacy.

Challenges and Limitations of Watermarking

While it can be a useful tool for copyright protection and content authentication, there are some issues to consider:

Vulnerability to Attacks

Watermarks are sensitive to assaults like removal, manipulation, and cloning. Attackers can remove or modify the watermark using various methods, such as filtering, cropping, or scaling, which can result in the loss of copyright protection or the validity of the content.

Difficulty in Maintaining Image Quality

Embedding watermarks can occasionally degrade the original image's quality. Adding a visible watermark, for example, can obstruct critical sections of the image, while embedding a digital watermark can produce noise or distortions that degrade the image's overall quality.

Compatibility With Different Image Formats

Compatibility concerns with various picture formats can hamper watermarking. Some image formats, such as JPEG, may not enable specific watermarking techniques, while others may not retain the watermark following picture alteration or conversion to a new format.

Thus, while watermarking can be useful for content protection and authentication, these limitations and obstacles must be considered before developing a watermarking system.