Fake Detection

Fake detection research focuses on identifying manipulated media, encompassing both sophisticated deepfakes (created using AI) and simpler "cheapfakes" (e.g., out-of-context images or misleading captions). Current efforts leverage deep learning models, including generative adversarial networks (GANs) and large language models (LLMs), to analyze visual and textual inconsistencies, often employing techniques like adversarial training and prompt engineering to improve detection accuracy. This field is crucial for combating misinformation and protecting against malicious uses of manipulated media, with implications for journalism, law enforcement, and social media platforms.