JPEG Frequency domain-based Image steganography using iterative prediction and decision rules to improved data integration

This paper proposes a novel steganographic method that employs a feedback mechanism to improve the efficiency and stealth of data hiding within the Discrete Cosine Transform (DCT) coefficients of JPEG images. This method enhances the correlation between the hidden message and the cover image, while minimizing the perceptible changes to the image. The system starts by dividing the cover image into blocks and applying DCT to each. It then evaluates the correlation between the hidden message and the DCT coefficients to identify potential data embedding points. A trained decision rules algorithm then chooses the optimal data embedding technique, considering factors like the size and location of the DCT coefficient within image blocks. Different embedding techniques are employed. The system subsequently generates feedback based on metrics such as image quality and data detectability, refining the decision ruls's effectiveness over time. By employing this dynamic approach, our system adaptively improves the data hiding process, enhancing capacity and minimizing detectability. This work opens new doors in the realm of steganography, presenting an intelligent system capable of adaptively embedding data with optimized stealth and efficiency.