CNN-BASED SYSTEM FOR SECURITY-ASSURED IMAGE WATERMARKING GENERATION FOR HEALTHCARE AND METHOD THEREOF

Abstract

The present invention relates to a CNN-based system for security-assured image watermarking, specifically designed for healthcare applications. The system employs a deep Convolutional Neural Network (CNN) for generating, embedding, and extracting digital watermarks in medical images such as MRI, CT, and X-ray scans. The watermark is generated from healthcare-specific data, encrypted using multi-layer encryption, and embedded in the image using a hybrid approach that combines spatial and frequency domain techniques. This method ensures that the watermark remains robust against common image processing operations like noise addition, compression, and cropping. The system includes image preprocessing to standardize quality, as well as modules for secure watermark extraction and verification. It also integrates features for adaptive watermark strength adjustment and noise resilience to enhance robustness. Designed to meet healthcare data protection regulations such as HIPAA, the system ensures secure handling and storage of medical images, making it suitable for telemedicine, clinical trials, remote diagnostics, and medical archiving. The invention provides an efficient, compliant, and highly secure solution for protecting sensitive healthcare data.

Specification

Description:[014] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[015] Referring now to the drawings, these are illustrated in FIG. 1, the present invention provides a comprehensive solution for embedding secure digital watermarks in medical images, ensuring data authenticity, integrity, and compliance with regulatory standards. The system consists of interconnected modules designed to work sequentially for efficient and robust watermarking. It begins with the acquisition of healthcare images (such as X-rays, MRI scans, or CT scans) and relevant watermark data, including patient information or unique identification codes, ensuring traceability and secure sharing. The input images undergo preprocessing to standardize dimensions, enhance quality, and filter noise, which prepares them for subsequent watermarking processes.
[016] In accordance with another embodiment of the present invention, the watermark generation module uses a deep Convolutional Neural Network (CNN) model to create a secure watermark from input data such as text, binary information, or healthcare-specific details like patient identification numbers or diagnostic information. Initially, the input data is encoded into a binary format before being processed by the CNN model. The network is trained to produce a watermark pattern that is highly resistant to common image processing operations such as noise addition, compression, and cropping. This robust design ensures that the watermark remains intact even if the image undergoes various modifications.
[017] In accordance with another embodiment of the present invention, a fter the watermark is generated, it is encrypted using a multi-layer encryption interface, adding an extra layer of security to the watermark data. The encrypted watermark is then embedded into the healthcare image using a deep CNN-based embedding approach. The embedding occurs in the transform domain, utilizing techniques such as Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT) to minimize any perceptual distortion in the original image. The CNN optimizes the embedding parameters, including the location and intensity of the watermark, to balance imperceptibility with robustness, ensuring that the watermark is not easily detected or removed by conventional image processing techniques.
[018] In accordance with another embodiment of the present invention, the watermark extraction process employs a trained CNN model to recover the watermark from the watermarked image. The extraction module is designed to accurately retrieve the embedded watermark, even if the image has been altered through operations like lossy compression, cropping, or rotation. Once the watermark is extracted, it is decrypted to obtain the original data, thereby maintaining the integrity and authenticity of the healthcare image. This module ensures that the system can verify the security and authenticity of the image under various conditions as shown in figure 2.
[019] In accordance with another embodiment of the present invention, To achieve uniformity across different medical imaging modalities (e.g., MRI, CT, X-ray), the system includes an image preprocessing module. This module standardizes the dimensions and quality of the input image, enhancing visibility through noise filtering and contrast adjustments without compromising the watermark's robustness. Preprocessing ensures that the image is optimally prepared for watermarking, leading to consistent and reliable results across various types of healthcare images.
[020] The system incorporates several security measures and compliance checks to ensure data protection. The multi-layer encryption technique used for securing the watermark helps prevent unauthorized access. The system supports advanced encryption interfaces such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) to further enhance security. Additionally, the system verifies the integrity of the extracted watermark by comparing it with the original data to detect any tampering or unauthorized alterations. Designed to meet healthcare data protection standards like HIPAA, the system ensures the secure handling, storage, and transmission of medical images, maintaining patient confidentiality and data integrity.
[021] The system introduces several innovative features to improve watermarking effectiveness. An adaptive interface based on CNN adjusts the watermark's strength according to the medical image's content, ensuring the watermark remains imperceptible while maximizing robustness. The watermark generation process incorporates noise-resilient design features to withstand noise addition and other common image alterations. Additionally, the system employs a hybrid domain watermarking approach, embedding the watermark in both the spatial and transform domains to enhance resistance against various types of attacks, utilizing spatial features and frequency-domain coefficients from DWT or DCT for the embedding process.
[022] The method involves a sequence of steps to securely embed and verify digital watermarks in healthcare images:
Input Acquisition: The process starts by obtaining a digital healthcare image, such as an X-ray or MRI scan, along with relevant data for the watermark, including patient ID or diagnostic details.
Preprocessing: The image undergoes preprocessing to enhance quality, standardize dimensions, and encode the watermark data into a binary format, preparing the image for the subsequent steps.
Watermark Generation: A CNN model generates a secure watermark pattern from the encoded data, which is then encrypted using multi-layer encryption to protect against unauthorized access.
Watermark Embedding: The encrypted watermark is embedded into the healthcare image using a CNN-based embedding interface. The embedding occurs in the hybrid domain, combining spatial and frequency techniques to improve robustness and imperceptibility.
Watermark Extraction: When authentication is needed, the watermarked image is processed by the CNN-based extraction module to recover the embedded watermark, even if the image has undergone modifications.
Verification: The extracted watermark is decrypted and compared with the original data to verify the integrity and authenticity of the image, ensuring it has not been tampered with.
[023] The system provides significant advantages for securing healthcare images in various scenarios, such as:
Telemedicine: Ensuring that medical images shared between healthcare providers and patients are secure and authentic.
Clinical Trials: Protecting the integrity of images used in clinical research, ensuring that research data remains uncompromised.
Remote Diagnostics: Safeguarding the accuracy and privacy of diagnostic images sent to specialists in remote locations.
Medical Image Archiving: Ensuring the long-term security and authenticity of archived medical images, preventing data corruption or tampering.
[024] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[025] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A system for security-assured image watermarking in healthcare, comprising:
a watermark generation module configured to utilize a Convolutional Neural Network (CNN) model to generate a secure watermark pattern based on input data, the input data comprising healthcare-specific information such as patient identification, diagnostic details, or institutional codes;
a watermark embedding module that encrypts the generated watermark using a multi-layer encryption interface and embeds the encrypted watermark into the healthcare image using a hybrid approach that includes both spatial and frequency domain techniques;
a watermark extraction module configured to decrypt the extracted watermark to retrieve the original data, ensuring the integrity and authenticity of the healthcare image by employing a trained CNN model to recover the embedded watermark from the healthcare image after processing;
an image preprocessing module for standardizing the dimensions and quality of the healthcare image, performing noise filtering, and contrast enhancement before embedding the watermark;
a noise-resilient watermark design feature, incorporating deep learning-based techniques to enhance the watermark's resistance to noise addition and other common attacks;
an adaptive watermark strength adjustor based on the content of the healthcare image, achieved using a CNN-based adaptive interface; and
security and compliance features that ensure the system meets healthcare data protection regulations, providing data integrity verification and encryption/decryption capabilities;
wherein the extraction module is capable of accurately extracting the watermark even if the image has undergone lossy compression, cropping, or other image processing operations.
2. The system as claimed in claim 1, wherein the watermark generation module encodes the input data into a binary format prior to generating the watermark pattern, ensuring that the watermark remains imperceptible and robust against attacks.
3. The system as claimed in claim 1, wherein the watermark embedding module utilizes a deep learning interface to optimize the embedding parameters, including the location and intensity of the watermark, to balance watermark imperceptibility and robustness.
4. The system as claimed in claim 1, wherein the hybrid watermark embedding approach uses Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT) techniques to embed the watermark in the frequency domain, thereby enhancing the watermark's resistance to image processing attacks.
5. The system as claimed in claim 1, wherein the security and compliance features include:
support for advanced encryption interfaces such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) for securing the watermark;
verification of the integrity of the extracted watermark by comparing it with the original input data to detect any potential tampering; and
compliance with healthcare data protection standards such as HIPAA.
6. A method for generating and embedding a secure watermark in healthcare images, comprising the steps of:
a) acquiring a healthcare image and relevant watermark data, such as patient information or diagnostic details;
b) preprocessing the healthcare image to enhance quality, standardize dimensions, and encode the watermark data into a binary format;
c) generating a secure watermark pattern using a CNN model trained to produce patterns that are robust against image processing operations;
d) encrypting the generated watermark using a multi-layer encryption interface;
e) embedding the encrypted watermark into the healthcare image using a CNN-based interface that operates in both the spatial and frequency domains;
f) extracting the watermark from the watermarked image using a trained CNN model for verification; and
g) decrypting the extracted watermark to compare it with the original data for integrity verification.
7. The method as claimed in claim 6, wherein the embedding process utilizes Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT) techniques to minimize perceptual distortion while maximizing the watermark's robustness.

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