# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 1 Introduction  
The development of deepfake technologies presents significant challenges for visual cognition in deep learning (DL) and raises serious concerns for visual information risks, as these synthetic media convincingly manipulate visual content, spreading misinformation [1, 2, 3]. The manipulated content may raise concerns about the potential abuse of technology and its consequences on politics, finance, and personal privacy [2, 4]. Although convolutional neural networks (CNNs) have achieved considerable success in computer vision tasks [5, 6, 7, 8] including deepfake detection, alternative approaches that combine machine learning and hand-made features are gaining popularity. In computer vision, technologies such as auto-encoders and generative adversarial networks (GANs) facilitate the generation of manipulated visuals. Deepfake images are often categorized into facial synthesis, attribute manipulation, identity swapping, and expression swapping, with CNN models commonly employed for video detection [4, 9]. To identify highly precise synthetic visual data as deepfake, an effective deepfake detection method is required [2, 10]. DL reduces human effort in feature engineering but increases complexity and interpretability due to high nonlinearity and input interactions. Traditional machine learning (ML) methods often sacrifice accuracy for interpretability due to their complexity and large data volumes. DLmethods are challenging to train and require computing resources, while ML methods are easier to evaluate and understand [11; 12]. To address these constraints, we encouraged to experiment and evaluate traditional machine learning techniques to detect deepfakes. Machine learning (ML) classifiers that use features such as local binary pattern (LBP) [13] and KAZE descriptor [14] offer promising alternatives to CNN-based methods for deepfake detection. LBP encodes texture information through local spatial patterns [13], while KAZE provides robust, noise-invariant descriptors [14] and key points, allowing the detection of minor deepfake artifacts that CNNs may overlook [12; 13; 14]. Traditional models can be improved in accuracy and resilience by integrating machine learning feature extraction methods [15]. Smaller datasets can benefit greatly from the use of LBP and KAZE features, which increase detection process transparency [16; 17]. To address the growing difficulties presented by deepfake technology, our hybrid approach--which integrates feature-based classifiers--offers a potential path ahead in differentiating real from fake visual data [18]. Deepfake detection solutions are limited for social media analysis due to heavy compression [19]. A compact model, based on ML classifiers, is needed for memory-constrained devices like smartphones [20; 21]. The proposed model, focusing on auto-encoder-based generated videos and keyframe identification, achieves high accuracy with minimal computational demands, making it suitable for memory-constrained devices and enhancing deepfake detection capabilities [22; 23]. This study aims to reduce computational costs in deepfake detection without substantial accuracy loss. Our proposed model targets compressed social media videos, focusing on the existing multi-feature fusion approach using multiple feature types (HOG, LBP, and KAZE) for a more comprehensive representation of visual data. By analyzing variations in visual artifacts, we achieve significant data reduction while preserving accuracy. The method integrates well-known ML feature extraction methods with diverse texture features, allowing effective training on limited datasets. We evaluated the proposed fusion model using the Face Forensic++ [24] and Celeb-DF [25] dataset, which replicates scenarios commonly found on social media platforms. The use of FaceForensics++ and Celeb-DF datasets aligns with the common practice in the field, allowing for a direct comparison with existing methods.