Cloud Image Security Enhancement through Tunable Steganography for Clone Attack Mitigation

As enterprises increasingly rely on cloud platforms to store and manage multimedia content, the threat of image cloning attacks where adversaries illegally duplicate and exploit stored images has become a critical concern. Traditional encryption, while essential, often introduces detectable patterns that may attract attackers. In response, this study introduces a Tunable Steganographic Framework named the Customizable Image Steganography Model (CISM), designed to conceal sensitive image data seamlessly within benign cover images. Leveraging the Integer Wavelet Transform (IWT), CISM operates in the frequency domain, enabling precise and resilient data embedding. A novel Pixel-Value Coding Algorithm ensures that the down-sampled secret image is embedded into high-frequency sub-bands of an up-sampled host image, preserving both image quality and data integrity. The method supports a two-phase embedding strategy—for primary secret content and auxiliary data enhancing its adaptability across security levels and use cases. For retrieval, inverse operations are performed to reconstruct the original image with minimal loss. Experimental results confirm CISM’s capability to provide strong confidentiality, low distortion, and flexible configuration, making it a compelling solution for defending against image cloning in enterprise cloud ecosystems. Furthermore, CISM offers dynamic scalability, has low bandwidth overhead, and continues to deliver reliable performance in a range of cloud scenarios. These features make it suitable for practical integration in security-sensitive, cloud-driven image processing applications.