Deep learning methods to detect image falsification

Abstract

The accessibility and advancement of digital image editing tools have enabled individuals to manipulate and create realistic images without a real basis, leading to novel forms of creative expression and new professions. However, this also raises concerns over the malicious use of these technologies in spreading disinformation and fabricated evidence. Deepfake videos, which are generated using deep learning techniques, have become a major concern due to their potential to spread false information and cause harm to individuals and society as a whole. Therefore, the development of accurate and efficient deepfake detection methods has become an urgent need. After a thorough review of deep learning-based approaches for detecting deepfake videos, the LRNet method was chosen as a basis for further research due to its high precision. The method is designed to analyze the temporal changes in a video and identify whether the video has been manipulated or not. Experiments were conducted using a publicly available dataset. The first step involved analyzing the impact of model parameters. A total of 135 combinations were analyzed by changing the block size, dropout rate, learning rate, and optimizer. Based on the results, the model’s performance was enhanced by reducing the initial dropout rate, decreasing the number of GRU hidden neurons, and adding additional linear and ReLU6 layers. Upon conducting and comparing the results, it becomes evident that the chosen and improved method achieves promising outcomes in deepfake detection. This demonstrates the effectiveness of the dual-stream RNNs and the calibration module in enhancing the accuracy of geometric feature detection over time. This approach offers a more reliable way to detect deepfake videos, which is essential for preventing their malicious use in various domains. The results show that our proposed improved method achieves higher accuracy in some scenarios and provides a valuable analysis on how detection models are affected by their parameters.