Large language models (LLMs) are revolutionizing how we create and consume information. But this power comes with a dark side: the potential for misuse in generating convincing fake content, or "deepfakes." How can we trust the information we read online when LLMs can create realistic yet fabricated news articles, product reviews, or even social media posts? Researchers are tackling this challenge head-on, developing techniques to detect LLM-generated text. One of the most promising approaches is watermarking, which involves embedding an invisible 'signature' within the text during its creation. A new research paper introduces "PostMark," a robust, post-hoc watermarking method for LLMs. Unlike previous techniques, PostMark doesn’t require access to the LLM’s inner workings. It can be applied *after* the text is generated, making it a more practical solution for widespread adoption. PostMark works by leveraging the semantic meaning of a text. It selects a list of words related to the text’s core concepts and then uses another LLM to subtly weave these words into the original text. This creates a hidden watermark that can be easily detected but is difficult for humans to notice or remove. Extensive testing shows PostMark to be highly effective, even when the text is paraphrased or translated. It maintains high detection rates while minimally impacting the overall quality and readability of the text. This approach marks a significant step toward ensuring trust and authenticity in the age of increasingly sophisticated AI-generated content. While promising, PostMark is not without its limitations. It relies on external APIs, which can be costly. However, the method's modular design allows for the use of open-source alternatives, making wider adoption a possibility. The ongoing research and development in this field are crucial. As LLMs become more powerful, so too must the tools we use to detect their misuse. PostMark represents a valuable contribution in the fight against deepfakes, paving the way for a more trustworthy and transparent online information ecosystem.