The proliferation of deepfake technology has raised significant concerns regarding the manipulation and authenticity of digital media. Addressing the urgent need for reliable detection methods, this research explores the application of deep learning techniques in identifying image-based deepfakes.[12] Specifically, this study delves into the utilization of convolutional neural networks (CNNs) and ResNet50, a residual neural network architecture, for discerning manipulated visual content.[3] The research methodology involves training and evaluating these deep learning models on diverse datasets comprising authentic and manipulated images. Through the implementation of transfer learning, the pre-trained ResNet50 model is fine-tuned on a deepfake-specific dataset to enhance its capacity for accurate detection. Key factors influencing the efficacy of these detection methods, such as dataset size, model architecture, and training parameters, are thoroughly analyzed and discussed. Evaluation metrics encompassing accuracy, precision, recall, and F1 scores are employed to assess the performance of the models in differentiating between real and deepfake images. The findings underscore the robustness of ResNet50 and CNN-based approaches in detecting image-based deepfakes, exhibiting promising results in identifying manipulated content across various contexts. Furthermore, insights into the limitations and potential areas for improvement in deepfake detection using these methodologies are presented, paving the way for future research endeavors in this critical domain.

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10. DEEPFAKES: THREATS AND COUNTERMEASURES SYSTEMATIC REVIEW Marwan Albahar, Jameel Almalki
11. Conceptual Understanding of Convolutional Neural Networks – A deep learning approach. Sakshi Indolia , Anil Kumar Goswami , S.P. Mishra , Pooja Asopa
12. Deep learning for deepfake creation and detection: A survey. Thanh Thi Nguyena, Quoc Viet Hung Nguyenb, Dung Tien Nguyena, Duc Thanh Nguyena, Thien Huynh-Thec, Saeid Nahavandid, Thanh Tam Nguyene, Quoc-Viet Phamf , Cuong M. Nguyen
13. https://neptune.ai/blog/performance-metrics-in-machine-learning-complete-guide Performance metrics in Machine learning
14. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53. A Comprehensive Guide to Neural Networks
15. https://datagen.tech/guides/computer-vision/resnet-50/ Resnet50: The basics and quick tutorial.
16. https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall#:~:text=Accuracy%20is%20a%20metric%20that,the%20total%20number%20of%20predictions. Accuracy vs. Precision vs. Recall in machine learning.

Deepfake Detection ResNet50 CNN Deep learning