## 4 Experimental Results
### Ablation Study  
**Effectiveness of image-permutation. ** The image disambiguation algorithm serves two crucial purposes: (1) preventing the disclosure of secret information through the corresponding error image, even if the training image set is leaked, and (2) ensuring that, without the key and inverse permutation algorithm knowledge, attackers cannot accurately reconstruct the correct information, even with black-box attack attempts. Table 4 illustrates a noticeable degradation in the image quality of cover-stego and secret-reconstructed image \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multirow{3}{*}{**RP**} & \multicolumn{4}{c}{**Cover/Stego Image Pair**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**DIV2K**} & \multicolumn{2}{c}{**COCO**} & \multicolumn{2}{c}{**ImageNet**} \\ \cline{3-8}  & & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** \\ \hline
4 bit-LSB & 50\% & 33.19 & 0.94 & 33.79 & 0.94 & 33.68 & 0.94 \\ \hline [PERSON] et al. [22] & 33.3\% & 30.70 & 0.92 & 30.18 & 0.91 & 32.68 & 0.93 \\ \hline DeepMIH [34] & 300\% & 34.13 & 0.94 & 34.29 & 0.94 & 33.39 & 0.93 \\ \hline CHASE\_WO & 300\% & 35.98 & 0.94 & 33.59 & 0.92 & 33.34 & 0.93 \\ \hline CHASE & 300\% & 33.09 & 0.91 & 31.34 & 0.90 & 30.03 & 0.92 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multirow{3}{*}{**RP**} & \multicolumn{2}{c}{**DIV2K**} & \multicolumn{2}{c}{**COCO**} & \multicolumn{2}{c}{**ImageNet**} \\ \cline{3-8}  & & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** \\ \hline
4 bit-LSB & 50\% & 30.81 & 0.90 & 32.04 & 0.91 & 31.26 & 0.90 \\ \hline [PERSON] et al. [22] & 33.3\% & 32.11 & 0.93 & 32.13 & 0.92 & 34.75 & 0.93 \\ \hline DeepMIH [34] & 300\% & 33.47 & 0.93 & 33.87 & 0.93 & 32.21 & 0.92 \\ \hline CHASE\_WO & 300\% & 36.02 & 0.95 & 34.41 & 0.94 & 32.07 & 0.93 \\ \hline CHASE & 300\% & 32.23 & 0.93 & 33.47 & 0.93 & 31.62 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comprehensive comparison of benchmarks across different datasets. Note for steganography methods with a relative payload greater than 100%, the evaluation metrics for cover-stego images consider the images after hiding, while the metrics for secret-reconstructed secret images represent the average across all extracted secret images. Arrows (\(\uparrow\)) indicate the direction of more effective changes in values. Figure 6: A visual comparison of our CHASE with 4 bit-LSB, [PERSON] et al. [22], and DeepMIH [34], showcasing both the stego images and the reconstructed secret images. The red boxes in the first column denote cropped regions for image steganography actually labeled as GT (Ground Truth) in the second column. The subsequent columns (from the third to seventh) display the steganographic image of different methods. The first row illustrates the effects of their stego images, while the second row shows the reconstructed secret images. pairs due to the inclusion of image-permutation. However, this sacrifice is justifiable in scenarios demanding high security. In Figure 7, the error images produced by various methods highlight the effectiveness of the image-permutation. Despite the challenging task of hiding a secret image with complex texture in a cover image, CHASE's error image exhibited no texture replication artifacts associated with the original secret image. In contrast, error images from 4 bit-LSB, [PERSON] et al. [22], DeepMIH [34], and CHASE_WO showed discernible texture replication artifacts, revealing the outline of the secret information and posing a risk of information leakage. In comparison, CHASE not only achieved a relatively larger hiding capacity but also maintained superior visual effects and security. **Effectiveness of the GAN.