# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 3 Proposed Fusion Model
### LBP Features  
The LBP is an advanced technique for extracting features from images for texture study due to its efficient handling of value variations and straightforward computational process [52]. LBP sets thresholds for neighboring pixels, enabling accurate spatial pattern extraction from images and transforming textual information into binary data for classification and detection [53]. LBP analyzes every pixel in an image by evaluating the relationship between each pixel and its surrounding pixels within a specified radius \(R\) distance away from it. If the neighboring pixel value exceeds that of the pixel level, a binary bit is assigned as 1; otherwise, it is marked as 0 [54].  
Given a grayscale image \(I\) of size \(M\times N\), the LBP feature for each pixel \((x,y)\) is computed via the following formula:  
\[\text{LBP}(x_{p},y_{p})=\begin{cases}1&\text{if }I(x_{p},y_{p})\geq I(x,y)\\ 0&\text{otherwise}\end{cases} \tag{1}\]  
For a pixel \((x,y)\), we compare its intensity \(I(x,y)\) with the intensities of its \(P\) neighboring pixels on a circle of radius \(R\). Let the intensities of these neighbors be \(\{I(x_{p},y_{p})\}P_{P=1}\). A binary value is assigned to each neighboring pixel. These binary values are concatenated to form a binary number, which is then converted to a decimal value. Compute the histogram of these LBP values over the entire image.  
\[H_{\text{LBP}}(k)=\sum_{x=1}^{M}\sum_{y=1}^{N}\delta(\text{LBP}(x,y),k),\quad k \in\{0,1,\dots,2^{P}-1\}\]  
where \((a,b)\) is the Kronecker delta function, which is 1 if \(a=b\) and 0 otherwise. Finally, the histogram is normalized with a small constant to prevent division by zero.  
The calculation used in this study is explained as follows: for a given pixel \((x_{p},y_{p})\), the intensity \(I(p_{i})\) in the center of the \((3\times 3)\) block is computed by comparing \(x_{p}\) to its 8 neighboring pixels. The texture classification process relies on illumination, translation, and rotational variance, while keyframes lack control over these attributes, focusing instead on uniform pattern representation. A uniform pattern, \(LBP(x_{p},y_{p})\), is more suitable. Preliminary experiments showed \(P=12\) and \(R=2\) provide the best performance for the feature descriptor. With regard to categorizing textures on the basis of their patterns in images or videos, the way the pattern is perceived is influenced by factors such as lighting changes, shifting positions, and orientations of the texture details. However, when we focus on moments in a sequence, the patterns are not affected by rotations or translations. Instead, they are determined by how colors and contrasts spread out over different frequencies and points in time, creating a consistent pattern overall. Therefore, using patterns such as \(LBP(x_{p},y_{p})\) is more suitable for detecting deepfake keyframes. To evaluate this, we conducted some experiments where we tried different values for the radius \(R\) as well as the neighboring pixel \(P\). After conducting our analysis, we determined that the values of \(P=12\) and \(R=2\) are suitable for the feature descriptor [54].  
Figure 1: General abstract of the proposed feature-level fusion method.  
These comparisons provide a binary vector that represents the connection between the intensity of one pixel and its neighbors. LBPs are widely utilized intensity-based features in various domains, including human detection [55; 56], facial recognition [55], background subtraction, and textured surface recognition [57]. The LBP operator is particularly appealing because of its computational simplicity [58]. However, a notable limitation of the LBP method is the extensive number of histogram bins needed, which reduces its efficiency for localized image patches. Despite this drawback, LBP remains effective for global image representations, making it a valuable tool for image classification tasks.