A Comparative Analysis of Machine Learning and Deep Learning Approaches in Deepfake Detection

Deepfakes refer to the visual media where the faces, bodily movements have been digitally altered using some software or program, this has proven to be more of a double edged sword as it also contributes towards content creation and media creation that may be used for positive purposes. To combat this situation, measures to detect deep fake in the media is a credible approach. This work showcases a comparative analysis among 3 Deep Learning as well as 3 Machine Learning algorithms in order to reach a conclusive state of determining the best algorithms that can be implemented for Deepfake detection. For the machine learning algorithms, KNN, SVM and Logistic Regression have been used whereas CNN, TCN and CNN + LSTM have been used for the Deep Learning Algorithm. Detection of deepfakes through these algorithms works by sequentially processing, analyzing and classifying the features on the basis of the dataset fed for the algorithms. The chosen metrics for performing a comparison between each of the algorithms are Accuracy and F1 Score. The development, implementation and comparison of the algorithms was carried out on Google Collab and Jupyter Notebook. Upon comparative analysis of the algorithms between each other, it was found that CNN had the highest accuracy and Fl-score of 0.9409 and 0.7225 respectively with KNN being the worst-performing algorithm with an accuracy 0.5770 and F1 score of 0.4088 respectively.