# High-Fidelity Steganography: A Covert Parity Bit Model-Based Approach
These high-frequency coefficients, which are less critical to the visual perception of the image, can be modified to incorporate hidden data without causing noticeable changes in the image's appearance. This makes DCT-based steganography particularly effective, as it allows for high-capacity data hiding while enhancing the quality of the stego image. The ability to use insignificant coefficients for data hiding ensures that the visual quality of the stego image remains high, making DCT an ideal method for secure and undetectable data embedding that balances capacity and quality. Another field in which the DCT transform is adopted is image super-resolution construction. DCT-based methods in image super-resolution construction exploit this transform's ability to separate an image into different frequency components, allowing for targeted enhancement of high-frequency details such as edges and textures. This approach benefits from the DCT's sparse representation, where most of the significant information is concentrated in a few coefficients, leading to improved image clarity and sharpness [15]. Compensating between capacity and stego quality has been a frequently studied topic in the steganography field over the past few years [16; 17]. Several papers have proposed new schemes that possess the ability to overcome the trade-off problem by exploiting the strong compaction property of the DCT and replacing the substantial amount of insignificant DCT coefficients with scaled-down secret pixels [5; 16; 18; 19]. On the other hand, there is a positive relationship between the security attribute and the stego quality attribute, as enhancing the quality of the stego signal and increasing the level of similarity between the stego signal and the cover signal improves the security level of the scheme. Therefore, another trade-off problem is revealed between security and capacity attributes: the higher the capacity, the lower the stego quality. and the lower the security level of the resulting scheme [20]. Model-based schemes are a recent DCT-based hiding approach. These schemes share the idea of maintaining the structure of the high-frequency region of the DCT domain rather than relying on the simple method of direct secret replacement adopted by recent DCT schemes. This new DCT model-based approach tends to model the less-significant DCT components and generate a new hosting signal from the generated model. Next, a scaled-down version of the secret signal is superimposed onto the newly generated hosting signal to produce the final stego signal. The objective of the superimposing process is to achieve a stego signal that is close to or identical with the cover signal, as superimposing a low-scaled signal on a larger-scale signal generates a signal with structure analogous to the larger-scale signal. This paper proposes a new DCT model-based image hiding scheme. The proposed scheme is based on the observation that a superposition process which adds the secret pixels to the polynomial signal to generate the stego signal is not the optimum solution to decrease the difference between the original cover signal and the stego signal. Instead, the proposed scheme adaptively chooses to add or subtract the secret pixels to or from the modeled pixels in order to match the original DCT coefficients of the cover signal. This procedure maximizes the similarity between the stego signal and the original cover signal, resulting in enhanced stego quality. This is evident from the failure of a deep learning classifier using a Convolutional Neural Network (CNN) model to differentiate between the cover images and stego images produced using the proposed technique. The CNN is not part of the proposed technique. The CNN is basically used just to test the performance of the proposed technique. The objective of the CNN is used to perform a steganalysis attack on stego images produced using the proposed technique; in other words, the objective of the CNN is to detect the presence of secret data in the stego image.