# Common Gabor Features for Image Watermarking Identification
## 3 The Proposed Methods  
Figure 7 shows the three main steps in the proposed method: pre-processing, feature extraction, and watermarking identification. The proposed technique steps are depicted in detail below. Figure 5: A two-dimensional Gabor filter with eight orientations and five scale [26]. Figure 6: The convolution operation of a Gabor filter with an image. Step 1: Preprocessing  
Here, for all images in the VOC2008 Database, three preprocessing operations were performed: (1) input the image, (2) image conversion, and (3) image embedding. Step 1.1: Input Image First, the image can be entered by using Matlab function I = Imread (image). Step 1.2: Image Conversion  
To reduce overall computing complexity, convert the RGB image to gray scale image (I') using Matlab function (rgb2 gray (I)) [28]. Step 1.3: Image Embedding  
For each gray scale image (I'), we generated the watermarked image using uMark ([[https://www.uconomix.com/Products/uMark/Default.aspx](https://www.uconomix.com/Products/uMark/Default.aspx)]([https://www.uconomix.com/Products/uMark/Default.aspx](https://www.uconomix.com/Products/uMark/Default.aspx)), accessed on 27 August 2021). uMark is a free utility for embedding a watermark into an image that is accessible for both Windows and Mac systems. The benefit of this software is that it allows for batch watermarking, which allows for the processing of 100 photos at once. In the generating procedure, the randomization was the randomization factor was considered. Step 2: Feature Extraction  
One of Gabor's advantages is that it reduces computing time. The Gabor filter's scale and orientation have a significant impact on classification accuracy. Mean squared energy and mean amplitude are derived throughout sale 3 and orientation 2 in our experiments. A gray-scale image's Gabor features are computed using the function \"PHASESYM\". Step 2.1: Apply Gabor  
As previously stated, apply 2D Gabor filters to each watermarked gray scale image I' of size M \(\times\) N pixels using formula (1). The following are the Gabor filter bank parameters: nscales = 3, nonientients = 2. Then, convolve each filter gu,v (x, y) with the image I' to generate the set of (3 \(\times\) 2 = 6) alternate representations of each image (response matrices). Step 2.2: Gabor Features Extraction  
Finally, mean squared energy and mean amplitude are extracted as feature vectors from those response matrices in order to obtain feature vector. The squared value of each matrix value in a response matrix is summed together to calculate mean squared energy. The sum of absolute values of each matrix value in a response matrix is being used to calculate the mean amplitude. The dimension of obtained mean squared energy feature vector is 1 \(\times\) 6. The dimension of obtained mean amplitude feature vector is 1 \(\times\) 6. The dimension of obtained combined features vector is 1 \(\times\) 12. Step 3: Watermark Identification  
Identification of image watermarking is a two-class problem, including host and watermark classes. As a result, we must figure out how to classify such images. Discriminant analysis (DA) and random forest are used to classify images. Step 3.1: Apply DA Classifier  
Figure 7: Proposed method Flowchart. The DA classifier was chosen as the first classifier for this research, and it is usually a decent first choice for developing classifiers. In many applications, discriminant analysis (DA) [29; 30] is one of the most used methodologies for classification. It is assumed that data is produced using distinct Gaussian distributions for various classes. DA creates a group membership prediction model. In order to produce optimum discrimination, the model discriminant function based on linear combinations of the predictor variables. The feature vectors are divided into two portions (training and testing data sets) that will be utilized to develop and train the DA model as well as the test and evaluate the final DA model.