Medical Image Hybrid Watermark Algorithm Based on Frequency Domain Processing and Inception v3

With the widespread use of digital medical images in recent years, the efficient and secure protection of sensitive data has become an urgent issue. Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. This article proposes a hybrid digital watermarking algorithm for medical images based on frequency domain transformation and deep learning convolutional neural networks. The algorithm combines the advantages of traditional frequency domain watermarking methods and deep learning techniques. Specifically, the algorithm first extracts feature vectors from medical images using discrete wavelet transform and discrete cosine transform, enhancing the robustness of the image watermark. It then integrates an improved Inception v3 network, optimizing the convolutional kernel design, thereby effectively enhancing the robustness of the watermarking process. To further improve the security of the watermark, the Logistic Map is used to scramble and encrypt the watermark information, and a hash function and XOR operation are employed for zero embedding of the watermark. The watermark extraction and decryption process relies on a key provided by a third party, enabling blind extraction of the watermark without requiring the original image. Through extensive experimental data analysis and comparisons with multiple existing algorithms, the results demonstrate that the proposed hybrid watermarking algorithm excels in terms of attack resistance and robustness, significantly improving the security and reliability of medical image watermarking.