Researchers at the University at Buffalo have explored the potential of large language models, such as ChatGPT and Google's Gemini, to detect deepfake images. Led by Siwei Lyu, SUNY Empire Innovation Professor of computer science and engineering, the study found that while these models lag behind state-of-the-art deepfake detection algorithms in accuracy, they offer a unique advantage: the ability to explain their decision-making process in plain language. When analyzing an AI-generated photo, ChatGPT correctly pointed out anomalies, such as blurry hair and abrupt transitions between the person and background. This semantic knowledge and natural language processing make ChatGPT a more user-friendly deepfake tool for both users and developers. The study's findings suggest that with proper prompt guidance, large language models can be effective at detecting AI-generated images, and their ability to provide explanations could revolutionize the field of deepfake detection.
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