Resilient Multimedia Embedding in DICOM Images: A Robustness Evaluation Under Multiple Attacks

Digital watermarking in medical imaging serves as a critical technique for ensuring data integrity, authentication, and copyright protection. This study explores an innovative approach to embed diverse file types, including text, PDF, video, and audio, into DICOM images using the Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) techniques. The primary objective is to evaluate the performance of these embedding techniques in terms of insertion time, extraction time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Squared Error (MSE). To assess the robustness of the embedded data, a series of attacks are applied to the DICOM images, including zero attack, salt and pepper attack, Gaussian attack, and speckle noise attack. The impact of these attacks on the integrity and retrieval of the embedded files is meticulously analyzed. The experimental analysis reveals that under Speckle Noise Attack (SNA), the character extraction accuracy remains relatively high across all file types, highlighting its minimal impact compared to other attacks. The results demonstrate the resilience of the embedded data against various forms of attacks, providing insights into the effectiveness and security of the proposed method. Speckle Noise Attack (SNA), LSB achieves higher accuracy in text files (85.76%) and PDF files (82–85%) compared to DCT, while DCT performs better in audio files with 86–86.5% accuracy. Overall, LSB is more effective for text and PDF watermarking, whereas DCT is more robust for audio files. The proposed framework uniquely enables the embedding of diverse multimedia content into DICOM images, addressing a critical gap in existing medical watermarking studies that focus mainly on text or visual data. This multi-format capability supports hospital systems in securing comprehensive patient records such as diagnostic notes, doctor-patient consultations, and procedural videos. Furthermore, the method demonstrates strong resilience against various attacks ensuring robustness and integrity validated by superior PSNR, SSIM, and MSE metrics. This comprehensive analysis not only validates the robustness of the embedding techniques but also lays the groundwork for future research in secure medical image processing.