Clearly articulate what constitutes malicious content in a video. This might involve defining specific categories of malicious content such as violence, explicit content, hate speech, etc.
Collect a diverse and representative dataset of videos that includes both malicious and non-malicious content. Ensure the dataset covers various types of malicious content and reflects the real-world scenarios the model will encounter.
Prepare the dataset for training by:
Choose a suitable machine learning or deep learning model for video analysis. Convolutional Neural Networks (CNNs)
Design the architecture of your model, taking into account the input format (frames, audio), the layers, and the output layer for classification. Consider using pre-trained models and fine-tuning them for your specific task.
Divide your dataset into training, validation, and test sets. Train the model using the training set, adjusting hyperparameters as needed. Monitor the performance on the validation set to avoid overfitting.
Evaluate the model on the test set to assess its performance. Metrics such as accuracy, precision, recall, and F1 score are commonly used. Pay attention to false positives and false negatives, as they are crucial in malicious content detection.
Iteratively fine-tune your model based on the evaluation results. Adjust hyperparameters, experiment with different architectures, or explore ensemble methods to improve performance.