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2024 47th International Conference on Telecommunications and Signal Processing (TSP)

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Improving the Security of Medical Imaging via DFT-based Reversible Watermarking and Deep Learning-based Zero-watermarking

This paper introduces a hybrid image verification and authentication technique, integrating reversible watermarking with zero-watermarking to improve the security of medical images. In zero-watermarking, a convolutional neural network extracts specific image features and merges them with a patient’s image to create a stego-image. To increase the robustness, the QR code is fused with the stego-image. The QR code is embedded into the image using a reversible watermarking technique into Regions of Non-Interest (RONI) detected by the K-means algorithm, ensuring the optimal region for the QR code embedding into the middle-frequency coefficients of the Discrete Fourier Transform. The experimental results validate the robustness of the proposed scheme despite applying different distortions to the watermarked image, including geometric transformations and image processing distortions. The results demonstrate that our method preserves image quality and recovers the watermark efficiently. The obtention of a low bit error rate and high normalized cross-correlation values evidences the efficiency.

Rodrigo Eduardo Arevalo-Ancona
Instituto Politecnico Nacional
Mexico

Manuel Cedillo-Hernandez
Instituto Politecnico Nacional
Mexico

 

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