Detecting Fake News Images Using a Hybrid CNN-LSTM Architecture

In today's digital world, images have become a double-edged tool in the dissemination of news; as much as they contribute to enriching honest content and communicating information effectively, they are increasingly being used to mislead the public and spread fake news. The ease of manipulating images and taking them out of their original context, or even creating them entirely with advanced techniques, gives them tremendous power in lending false credibility to false narratives, taking advantage of the human eye's tendency to believe what it sees and the image's superior ability to directly evoke emotions. These misleading images, which are often difficult to debunk with the naked eye, spread at lightning speed across digital platforms, allowing fake news to reach and influence large audiences before it can be verified. However, they tend to generate inaccurate reports. This study proposes a model architecture to detect fake news images. Machine learning and deep learning algorithms were used. The deep learning models are used depending on conventional neural nets (CNN), long short-term memory (LSTM) and a hybrid model that combines CNN and LSTM frameworks on Google Cloud. The hybrid model was able to categorize news with better accuracy than using each model individually. The model was tested and trained on a dataset for classifying fake news images. We used different evaluation metrics (precision, recall, F1 metric, etc.) to measure the efficiency of the model.