Nowadays, social media platforms play a significant role in real-world events, which can cause both positive and negative effects. The popularity of image-based content on social media has been dramatically increased, which brings the problem that the quality of content is rather spotty. Therefore, automated techniques of identifying fake images have drawn significant attention. The traditional detection methods focus on the elements' consistency of the image, which requires massive computing resources and huge datasets for pairs of real and fake images. Many studies on detecting rumors on social media showed that there are propagation patterns for the spreading of fake content that can be used as clues of detection. Thus, the proposed approach attempts to characterize the propagation patterns of fake images on social media using several user features and tweet features. The detection model applies recurrent neural networks to capture the variation of suggested features along the propagation path over time. The results of the experiment on a Weibo dataset of image tweets show that the model can achieve 89% accuracy in classifying fake images from real ones. Moreover, the model already reaches high performance as the detection deadline is smaller than 24 h, which demonstrates the strong capability of early detection. The positive outcomes indicate that the proposed detection model has great potential to be further developed to an automated technique that can be used in classifying real images from fake images posted on social media.
- Fake images
- recurrent neural network (RNN)
- social network