# Stop when we have V-1 edges in the MST if len(mst) == self.V - 1: break returnmst  
Figure 14: Visible comment method: Illustration. Figure 13: Visible comment method: Illustration. To evaluate the file size overhead associated with various steganography methods, we executed steganography-implemented Python functions on strings of varying lengths, from 1 to 100 characters, to determine the additional characters required by each method. The results are depicted in Figure 15.  
Notably, the method that alternates between small and capital letters introduces no overhead, while the method involving comment character modification exhibits a constant overhead. Conversely, methods that utilize white spaces and visible commands generally show an overhead that increases with the length of the string to be implanted, up to a certain maximum value. Specifically, the overhead for white space increases up to the number of lines; beyond this point, if the length of the text to implant exceeds the text length, only part of the comment can be implemented. For the visible comment method, the overhead continues to increase until it reaches the size of the comment, while, for comment modification, the overhead increases until it reaches the number of comments in the file. In addition, we tested whether popular LLMs can recognize the suggested hidden steganographic messages, and Table 10 summarizes our results. While some forms of text steganography are easily detected by LLMs, methods that generate code resembling standard programming syntax--such as utilizing various comment types to encode boolean information--pose greater challenges for detection. Notably, the recent implementation of watermarking in AI-generated code, as demonstrated in the Gemini chatbot by [63] ([[https://gemini.google.com/](https://gemini.google.com/)]([https://gemini.google.com/](https://gemini.google.com/)) accessed on 29 December 2024), highlights the practical application of generative watermarking. This technique, known as SynthID-Text [20], subtly alters the generated text without compromising its \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **[PERSON]** & **Deepseek** & **Gemini** & **Claude** & **Chatgpt o1** & **Chatgpt 4o** \\ \hline Whitespaces & V & V & V & V & V & V \\ Comments types & X & X & X & V & X & V \\ Capital \& small & V & V & V & V & X & V \\ letters & V & V & V & V & V & V \\ Additional letter & V & V & V & V & V & V \\ Visible comment & V & V & V & V & V & V \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the ability of LLMs to detect hidden steganographic messages in text, where ’V’ indicates detection and ’X’ indicates non-detection. Figure 15: Comparison of overheads associated with different steganography methods. quality, accuracy, or generation speed. By embedding imperceptible yet identifiable markers, this approach facilitates the reliable identification of synthetic text. Additionally, the integration of watermarking with speculative sampling has undergone extensive evaluation, confirming its ability to maintain AI-generated content quality while enhancing transparency and accountability in digital communication. However, another pertinent question is how to develop a mechanism that fosters cooperation between the company behind the LLM and educational teams in a way that prevents the development of universal methods for identifying and removing embedded watermarks. In our previous study [12], we developed a method for structured cooperation between educators and AI companies through a licensing framework. This framework supports assignment-specific watermarking, automated reporting, enhanced monitoring, and customizable techniques to uphold academic integrity. We proposed three approaches: The first involves signals that can be independently produced by an AI tool and recognized by any teacher or student, with or without AI detection software.