Data Augmentation-based Novel Deep Learning Method for Deepfaked Images Detection

Document Type


Source of Publication

ACM Transactions on Multimedia Computing Communications and Applications

Publication Date



Recent advances in artificial intelligence have led to deepfake images, enabling users to replace a real face with a genuine one. deepfake images have recently been used to malign public figures, politicians, and even average citizens. deepfake but realistic images have been used to stir political dissatisfaction, blackmail, propagate false news, and even carry out bogus terrorist attacks. Thus, identifying real images from fakes has got more challenging. To avoid these issues, this study employs transfer learning and data augmentation technique to classify deepfake images. For experimentation, 190,335 RGB-resolution deepfake and real images and image augmentation methods are used to prepare the dataset. The experiments use the deep learning models: convolutional neural network (CNN), Inception V3, visual geometry group (VGG19) and VGG16 with a transfer learning approach. Essential evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix and AUC-ROC curve score) are used to test the efficacy of the proposed approach. Results revealed that the proposed approach achieves an accuracy, recall, F1-score and AUC-ROC score of 90% and 91% precision, with our fine-tuned VGG16 model outperforming other DL models in recognizing real and deepfakes.




Association for Computing Machinery (ACM)


Computer Sciences


Computing methodologies, Artificial intelligence, Natural language processing, Language resources, Security and privacy, Cryptography, Information-theoretic techniques

Indexed in Scopus


Open Access


Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license