Imagine sending a secret message hidden within a seemingly ordinary text. This isn't science fiction, it's the realm of steganography. Current methods, however, face challenges in real-world use. They often produce texts easily flagged by automated detectors, especially on platforms like social media. This is because these methods rely heavily on language models (LLMs) that don't perfectly mimic true human writing. A new technique called FreStega aims to change that. It acts as a plug-and-play enhancement for existing steganography methods, making hidden messages even more invisible. FreStega works by dynamically tweaking the LLM's output in two ways. First, it adjusts the "temperature" of the text generation, essentially controlling the randomness and making the text appear more natural and diverse. Second, it subtly aligns the writing style with real text from the target platform (like a social media feed or a specific author's works), making it blend seamlessly with the surrounding content. The results are impressive. FreStega boosts the capacity for hidden information by over 15% while simultaneously making the secret-carrying text harder to detect. Even with limited samples of the target writing style, FreStega can quickly adapt and make the disguised text indistinguishable from genuine human writing. This opens up interesting possibilities for secure communication, but also raises questions about the future of online privacy and the detection of hidden content. As FreStega and similar methods evolve, the line between public and private communication becomes increasingly blurred, challenging our assumptions about online security and the nature of digital information itself.