Hurdle Faced: Dataset Accessibility & Framework Integration

One of the biggest challenges we faced during the development of our project was the limited accessibility to reliable, labeled datasets for deepfake detection and fake news analysis. Most publicly available datasets were either outdated, lacked diversity, or had licensing restrictions, which slowed down our model training and testing process. In addition, connecting multiple frameworks—like TensorFlow for model training, OpenCV for media processing, and NLP libraries for text analysis—created compatibility issues and made the pipeline unstable.

How We Solved It:

To overcome the dataset hurdle, we spent time manually curating a hybrid dataset from multiple trusted sources and used data augmentation techniques to increase its size and variability. For the integration challenges, we modularized our codebase and used intermediate APIs and wrapper scripts to ensure smooth communication between different frameworks. Docker was also helpful in creating a stable environment where all dependencies could coexist without conflict. These steps helped us build a reliable and scalable system despite the initial roadblocks.