# High-Fidelity Steganography: A Covert Parity Bit Model-Based Approach
## 4 Proposed Scheme
### Data Embedding
Despite this, MB2 can be broken using a Markov Process (MP), as shown in [44]. As deep learning methods gain traction, techniques such as transformer networks, Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs) have proven to be highly effective for steganalysis. These methods can automatically extract features from training data, eliminating the need for manually designed features, which can sometimes be flawed or misleading. The structure of the developed CNN is shown in Figure 15. The steganalysis test on the proposed scheme is depicted in Figure 16. As can be seen in Figure 16, the CNN was unable to correctly classify the input images or to detect the presence of hidden data, achieving an accuracy of only 50%. In contrast, the same CNN efficiently detected the existence of secret data when using the coefficient replacement hiding strategy, as shown in Figure 17, reaching a high accuracy level of around 90%. Figure 15: Design of the CNN detector. ## 6 Conclusions

This paper introduced a novel approach to DCT model-based steganography techniques. Instead of completely altering the high-frequency DCT coefficients, as in recent coefficient replacement strategies, the proposed method aims to preserve their structure. First, the high-frequency coefficients undergo polynomial modeling; then, the scaled-down secret samples are added to or subtracted from the polynomial-generated signal to reduce the error between the polynomial-generated signal and the original high-frequency DCT signal. Experimental results demonstrate the efficacy of this approach, showing improved imperceptibility. Furthermore, steganalysis testing using a CNN indicates an advanced

Figure 16: CNN steganalysis test on stego images generated using the proposed scheme. Figure 11:0 CNN steganalysis test on stego images generated using the coefficient replacement hiding strategy. security level, as the CNN failed to differentiate between cover and stego images even at high embedding capacities. Future work will focus on reducing the modeling error to further improve imperceptibility and security in model-based hiding schemes. Conceptualization, All authors; methodology, M.B. ; software, M.B. ; writing--original draft preparation, M.B. ; writing--review and editing, M.B. ; supervision, T.R. and I.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. No data or dataset was generated. The authors declare no conflict of interest. ## References

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