# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Comparative Analysis  
Our experimental results demonstrate significant improvements over existing approaches in linguistic steganography. Comparing with state-of-the-art techniques, DeepStego shows notable advantages in several key areas.  
Figure 4 shows a bar chart comparing detection rates using different steganalysis methods:  
The CNN-based approaches reported by [PERSON] et al. (2019) achieve detection accuracy of 0.911 and recall of 0.952 for 1 bit/word encoding. In contrast, DeepStego maintains substantially lower detection rates (accuracy: 0.6351, recall: 0.4503), indicating superior concealment capabilities. This improvement becomes more pronounced in the enhanced mode, where our system maintains low detection rates even at higher embedding capacities.  
For BERT-LSTM-Att based detection ([PERSON] et al., 2014), previous work shows accuracy rates of 0.786 on Twitter datasets. DeepStego achieves comparable or better results (accuracy: 0.7644) while supporting higher embedding capacities. Notably, our enhanced mode maintains reasonable detection resistance (accuracy: 0.9432) despite doubling the bits per word.  
The most significant achievement is demonstrated against Bi-LSTM-Dense detection ([PERSON] et al., 2014), where DeepStego achieves perfect confusion rates (accuracy: 0.5000, recall: 0.0000). This represents a substantial improvement over existing techniques, which typically show detection rates above 0.783.  
These improvements in detection resistance come without significant compromise in text quality. For example, Figure 5 shows a radial chart comparing three text quality metrics. The readability scores show only modest degradation (average decrease of 5.78 points), comparing favorably with existing approaches that often show more sub-stantial quality reduction at higher embedding rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Message Size** \\ **(Bits)** \\ \end{tabular} & \begin{tabular}{c} **Mode** \\ **Score** \\ \end{tabular} & \
\begin{tabular}{c} **Readability** \\ **Score** \\ \end{tabular} &
\begin{tabular}{c} **Natural Language** \\ **Coherence** \\ \end{tabular} \\ \hline \multirow{2}{*}{128} & Original & 29.18 \(\pm\) 11.49 & 0.892 \(\pm\) 0.043 & 0.945 \(\pm\) 0.028 \\  & Stego & 23.40 \(\pm\) 10.51 & 0.863 \(\pm\) 0.052 & 0.912 \(\pm\) 0.035 \\ \hline \multirow{2}{*}{256} & Original & 32.74 \(\pm\) 8.86 & 0.901 \(\pm\) 0.038 & 0.956 \(\pm\) 0.024 \\  & Stego & 26.99 \(\pm\) 8.98 & 0.878 \(\pm\) 0.045 & 0.923 \(\pm\) 0.031 \\ \hline \multirow{2}{*}{512} & Original & 32.76 \(\pm\) 8.90 & 0.899 \(\pm\) 0.041 & 0.951 \(\pm\) 0.026 \\  & Stego & 25.98 \(\pm\) 7.32 & 0.871 \(\pm\) 0.047 & 0.918 \(\pm\) 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Text quality assessment.  
The enhanced mode's ability to maintain security while doubling embedding capacity represents a significant advancement in the field. Figure 6 shows the performance comparison results for different message sizes. Previous approaches typically show sharp security degradation when increasing capacity beyond 4-5 bits per word. DeepStego maintains effective concealment even at 9.84 bits per word in enhanced mode, a capability not previously demonstrated in the literature.  
Figure 4: Steganalysis detection resistance: X-axis—steganalysis methods; Y-axis—methods of linguistic steganography; Z-axis—detection accuracy (lower is better); shows DeepStego’s superior resistance across all detection methods.  
Figure 5: Text quality metrics: Axes—readability, natural language, semantic coherence; demonstrates DeepStego’s balanced quality preservation.