Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network

• Published in: Computational intelligence and neuroscience 2021, Vol.2021 (1), p.3111676-3111676
• Main Authors: Shad, Hasin Shahed, Rizvee, Md. Mashfiq, Roza, Nishat Tasnim, Hoq, S. M. Ahsanul, Monirujjaman Khan, Mohammad, Singh, Arjun, Zaguia, Atef, Bourouis, Sami
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Celebrities; Comparative analysis; Comparative studies; Datasets; Deception; Deep learning; Deepfake; Humans; Image detection; Image processing; Learning algorithms; Machine Learning; Methods; Neural networks; Neural Networks, Computer; ROC Curve; Social discrimination learning; Social Media; Social networks; Social research; Support vector machines
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2021/3111676

Generation Z is a data-driven generation. Everyone has the entirety of humanity’s knowledge in their hands. The technological possibilities are endless. However, we use and misuse this blessing to face swap using deepfake. Deepfake is an emerging subdomain of artificial intelligence technology in which one person’s face is overlaid over another person’s face, which is very prominent across social media. Machine learning is the main element of deepfakes, and it has allowed deepfake images and videos to be generated considerably faster and at a lower cost. Despite the negative connotations associated with the phrase “deepfakes,” the technology is being more widely employed commercially and individually. Although it is relatively new, the latest technological advances make it more and more challenging to detect deepfakes and synthesized images from real ones. An increasing sense of unease has developed around the emergence of deepfake technologies. Our main objective is to detect deepfake images from real ones accurately. In this research, we implemented several methods to detect deepfake images and make a comparative analysis. Our model was trained by datasets from Kaggle, which had 70,000 images from the Flickr dataset and 70,000 images produced by styleGAN. For this comparative study of the use of convolutional neural networks (CNN) to identify genuine and deepfake pictures, we trained eight different CNN models. Three of these models were trained using the DenseNet architecture (DenseNet121, DenseNet169, and DenseNet201); two were trained using the VGGNet architecture (VGG16, VGG19); one was with the ResNet50 architecture, one with the VGGFace, and one with a bespoke CNN architecture. We have also implemented a custom model that incorporates methods like dropout and padding that aid in determining whether or not the other models reflect their objectives. The results were categorized by five evaluation metrics: accuracy, precision, recall, F1-score, and area under the ROC (receiver operating characteristic) curve. Amongst all the models, VGGFace performed the best, with 99% accuracy. Besides, we obtained 97% from the ResNet50, 96% from the DenseNet201, 95% from the DenseNet169, 94% from the VGG19, 92% from the VGG16, 97% from the DenseNet121 model, and 90% from the custom model.