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Detecting Deepfakes : Fine-tuning VGG16 for Improved Accuracy

Santhis, Ishaan
http://orcid.org/0009-0004-6371-7399
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712914093648037

Abstract Details

2024, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
The continuous threat of deepfakes, cleverly crafted deceptions masquerading as reality, necessitates cutting-edge detection methods. While there are many methods available, this project dives into the realm of fine-tuning the VGG16 convolutional neural network (CNN) and synergistically integrating Natural Language Processing (NLP) to unveil deepfake images effectively. By using the Keras API and machine learning principles, we empower the model to discern authentic images from their manipulated counterparts, drawing inspiration from real-world cases like the notorious Jennifer Aniston deepfake scam. Firstly, we establish a robust foundation for feature extraction by pre-training the VGG16 architecture on vast image datasets. Subsequently, we meticulously curate a comprehensive deepfake image dataset encompassing diverse manipulation techniques and real-world scenarios. This tailor-made dataset fuels the fine-tuning of specific VGG16 layers, accurately crafting a model with exceptional generalizability. Intriguingly. The project rigorously evaluates the fine-tuned VGG16 model's performance on unseen deepfakes through a battery of meticulous metrics, including accuracy, and loss while detecting the deepfakes. We delve into a comprehensive comparison, carefully analyzing these results not only against the baseline performance of a model I created from scratch, the untrained VGG16, the VGG16 after I applied transfer learning. This project aspires to make a significant contribution to the ongoing battle against deepfakes by showcasing the remarkable potential of fine-tuning the VGG16 that helps us in achieving superior detection accuracy. By thoroughly incorporating real-world examples and harnessing the synergistic power of CNNs, we strive to develop a robust and adaptable solution capable of combating the ever-evolving landscape of deepfakes. Ultimately, this endeavor aims to safeguard online safety and trust, mitigating the detrimental effects of deepfakes on society.
Yizong Cheng, Ph.D. (Committee Chair)
William Hawkins, Ph.D. (Committee Member)
Jun Bai, Ph.D. (Committee Member)
36 p.
Computer Science
VGG16; Transfer Learning; Data Augmentation; CNN; Deep Learning; Accuracy

Recommended Citations

Citations

  • Santhis, I. (2024). Detecting Deepfakes : Fine-tuning VGG16 for Improved Accuracy [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712914093648037

    APA Style (7th edition)

  • Santhis, Ishaan. Detecting Deepfakes : Fine-tuning VGG16 for Improved Accuracy. 2024. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712914093648037.

    MLA Style (8th edition)

  • Santhis, Ishaan. "Detecting Deepfakes : Fine-tuning VGG16 for Improved Accuracy." Master's thesis, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712914093648037

    Chicago Manual of Style (17th edition)

ucin1712914093648037
48
© 2024, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.