# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 3 Proposed Fusion Model
### Proposed Feature Fusion and Classification  
The process of combining LBPs and KAZE features for image classification involves extracting two distinct sets of features from a single image, merging these feature sets into a unified feature vector, and then using this combined vector to train a classifier. This procedure improves classification performance, particularly for detecting deepfake content by utilizing the included strengths of KAZE (keypoint detection and description) and LBP (texture analysis). The proposed fusion model is a comprehensive method for image classification that integrates LBP or HOG and KAZE features, followed by the selected classifier. Initially, the algorithm extracts LBP or HOG features, which capture texture features with the distribution of binary patterns in the neighborhood of each pixel. Mathematical representations (Equations 2 and 3) by the LBP histogram are used to normalize and achieve uniform feature scaling. Concurrently, KAZE features are extracted by detecting key points and computing their descriptors, effectively capturing local invariant features. These descriptors are integrated into a single vector, which is then truncated or padded to maintain a consistent feature length. The LBP and KAZE feature vectors are combined to create a single feature representation for each image, utilizing the improved strengths of both feature extraction methods. The process of merging the LBP and KAZE features is explained in these steps. First, we extract the LBP feature vector \(F_{L}BP\) from the histogram \(H_{L}BP\) by employing the following formula:  
\[F_{\text{LBP}}=H^{\text{LBP}}(0),H^{\text{LBP}}(1),\ldots,H^{\text{LBP}}(2^{P} -1) \tag{2}\]  
Second, the KAZE feature vector \(F_{KAZE}\) is extracted from the concatenated descriptor vectors \(D\) by \(F_{KAZE}=D\). Finally, the \(F_{LBP}\) and \(F_{KAZE}\) feature vectors are concatenated as follows:  
\[\mathbf{F}_{\text{combined}}=[\mathbf{F}_{\text{LBP}},\mathbf{F}_{\text{KAZE}}] \tag{3}\]  
The integration of LBP and KAZE features improves the algorithm's robustness and accuracy in deepfake classification tasks, especially when detecting false or real images. LBP features improve in detecting texture patterns, which are important in distinguishing between real and fake images because deepfakes often possess irregular or inconsistenttextures. In contrast, KAZE features retrieve fine-grained keypoints, which is necessary for detecting minor modifications that may not greatly alter texture but have an impact on the structural integrity of the image. The proposed fusion model improves the classifier's capability to identify deepfakes by integrating these two feature sets to provide a deeper and more discriminative feature space. In experiments utilizing fake images, the improved detection capabilities and decreased false positives demonstrate how the integration of texture-based and keypoint-based features results in the improvement of classification accuracy. The proposed fusion model improves performance on deepfake detection tasks by utilizing the advantages of KAZE (robust keypoint descriptors) and LBP (sensitive to textures). Furthermore, Algorithm 1 is expressed to provide a more detailed explanation from the perspective of implementation. ```
0: Set of images \(\{I_{1},I_{2},\ldots,I_{N}\}\), corresponding labels \(\{y_{1},y_{2},\ldots,y_{N}\}\). 0: Classification accuracy. 1:functionExtractLBPFeatures(\(I\), \(R=3\), \(P=24\))
2: Convert image \(I\) to LBP and HOG using radius \(R\) and \(P\) points. 3:return\(\hat{H}_{LBP/HOG}\)
4:endfunction
5:functionExtractKAZEFeatures(\(I\), \(M=64\))
6: Detect keypoints in \(I\) using KAZE. 7: Compute descriptors for each keypoint and Concatenate descriptors into \(\mathbf{D}\).