DEEPFAKE DETECTION USING TRANSFER LEARNING-BASED XCEPTION MODEL

Justification of the purpose of the research. In recent times, several approaches for face manipulation in videos have been extensively applied and availed to the public which makes editing faces in video easy for everyone effortlessly with realistic efforts.  While beneficial in various domains, these methods could significantly harm society if employed to spread misinformation. So, it is also vital to properly detect whether a face has been distorted in a video series. To detect this deepfake, convolutional neural networks can be used in past works. However, it needs a greater number of parameters and more computations. So, to overcome these limitations and to accurately detect deepfakes in videos, a transfer learning-based model named the Improved Xception model is suggested. Obtained results. This model is trained using extracted facial landmark features with robust training. Moreover, the improved Xception model's detection accuracy is evaluated alongside ResNet and Inception, considering model loss, accuracy, ROC, training time, and the Precision-Recall curve. The outcomes confirm the success of the proposed model, which employs transfer learning techniques to identify fraudulent videos. Furthermore, the method demonstrates a noteworthy 5% increase in efficiency compared to current systems.