## 4 Experimental Results
### Steganalysis  
Steganalysis serves as a countermeasure to steganography, detecting concealed information within carrier media. We evaluated the security of our steganography method against both statistical and deep learning-based steganalysis techniques.  
**Statistical steganalysis.** We employed StegExpose [46], a widely utilized steganalysis tool that relies on statistical image characteristics, to assess the anti-steganalysis capabilities of our CHASE method. To evaluate its performance, we generated 500 stego images using CHASE and selected an additional 1000 clean images for the test set. The StegExpose tool was applied with threshold adjustments between 0 and 1. The true positive rate (TPR) and false positive rate (FPR) were calculated, leading to the creation of the receiver operating characteristic curve (ROC) shown in Figure 8. The orange solid line represents our CHASE method's ROC, while the dark blue dotted line represents random guessing method. The assessment of steganography method detection by steganalysis tools can be framed as a two-classification task, with the Area Under the Receiver Operating Characteristic (ROC) Curve, denoted as AUC, serving as a widely used metric for evaluating the performance of such models. A higher AUC value, closer to 1, indicates superior detection performance, while a value nearing 0.5 suggests performance akin to random guessing. The AUC value of our CHASE method, at 0.53, underscores its strong resistance to steganalysis, affirming its high level of security. StegExpose hardly detect whether secret information was present in the stego images generated by CHASE.  
**Deep learning-based steganalysis.** The rise of deep learning has advanced steganalysis technology. In this section, we employed SRNet [47] and Zhu-Net [48], two state-of-the-art deep learning-based image steganalysis networks, to evaluate our CHASE method and benchmarks. Differing from the conventional approach of training the network with common steganography algorithms and then utilizing the model to detect different methods, we adopted a novel image steganalysis method proposed by [PERSON] et al. [49]. This method involves directly using distinct cover-stego image pairs generated by each steganography method as training sets for the network. The number of training sets was progressively increased to examine the network's detection capabilities under varying quantities of training sets.  
We generated 1500 stego images through both CHASE_WO and CHASE, forming their respective test sets with corresponding clean cover images. During the training of SRNet and Zhu-Net, the batch was set to 1, and other hyperparameters were configured according to their respective papers. Figure 9 illustrates changes in the detection accuracy  
Figure 8: Using the StegExpose tool to generate an ROC curve for our CHASE.  
of the steganalysis model as the number of training sets increases. The left image displays the detection effect under SRNet, while the results for Zhu-Net are on the right.  
Regardless of SRNet or Zhu-Net, our CHASE method exhibited superior anti-steganalysis capabilities compared to other methods. Under SRNet, even with 200 pairs of training samples, the detection accuracy of our CHASE method surpassed existing methods, with the SOTA method DeepMIH showing an 8% lower accuracy and Zhu-Net showing a 7.9% lower accuracy. Additionally, the unscrambling version CHASE_WO demonstrated an average 11.86% and 10.2% higher detection accuracy on SRNet and Zhu-Net, respectively, compared to the scrambling version CHASE. This discrepancy may be attributed to the distinct deep learning methodologies employed by SRNet and Zhu-Net to analyse residual information in the cover/stego image for prediction. As depicted in Figure 9, compared to other steganography methods, obtaining the residual image by CHASE from the cover/stego image is challenging for an attacker, resulting in enhanced resistance against steganalysis, even at large-capacity steganography scales.