Abstract
Nowadays, the Deepfake technology is mainly used to harm people’s reputations and can trick the face recognition system by swapping faces between people, raising significant security concerns. Thus, methods for detecting Deepfake are crucial. The recent methods for Deepfake detection have performed well in distinguishing real content from fake content. Some research employed the Transformer technique, commonly used in natural language processing (NLP), to enhance performance. Therefore, this paper proposes a novel deepfake detection method that transforms extracted features into words and utilizes NLP techniques for deepfake classification. We employed a fine-tuned pre-trained Convolutional Neural Network (CNN) model to extract features from the face images in the videos. These extracted features are labeled based on grouping methods, such as mean and standard deviation (SD). Tokenization and classification are then performed using Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). Additionally, Bidirectional Encoder Representations from Transformers (BERT) is used as another tokenizer and classifier to compare the performance of deepfake detection between the traditional model and the NLP model. The result states that the method using BERT as a tokenizer and classifier with Mean and SD grouping method shows better efficiency, achieving 99.57% on the Roc Curve, 99.58% Accuracy, 99.18% Precision, 100.00% recall, and 99.59% F-measure.
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