Benchmarking DeepFake Detection on Social Media: Real-World Dataset and Case Study

DeepFakes, which are fake videos, images, and audio clips designed to look real, have become a serious problem on social media. People can easily be misled by this kind of content, which often spreads false information, damages reputations, and tricks businesses as well as ordinary users. Although researchers have developed many tools to detect DeepFakes, most of these tools have been tested only in perfect laboratory conditions. Social media, however, is a much more chaotic environment, full of low-quality, compressed, and constantly reshared content. In this study, we wanted to find out how well these detection tools actually work in real-life social media platforms like Instagram, TikTok, YouTube, and Facebook where people interact every day. We built a large and realistic collection of videos, audio clips, and images directly from these platforms to reflect what users typically experience—blurry visuals, noisy sounds, and heavily compressed files. We tested several popular DeepFake detection models to measure how accurately, quickly, and reliably they can spot fake content in these everyday conditions. Our results show that even the most advanced detection tools lose about fifteen to twenty percent of their accuracy when working with social media content. Some tools, such as LaDeDa, are fast enough to work in real time on mobile phones but cannot catch all DeepFakes. We also explored a real Instagram case where a fake content campaign spread widely, showing that fully automated systems still struggle to catch every piece of manipulated content. Sometimes, human review is still necessary. Overall, this research emphasizes the urgent need for smarter, faster, and more adaptable DeepFake detection systems that can truly handle the way people share and consume information on social media.