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.

License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average.