AI for Medical Image Security: A Comprehensive Review of Techniques and Challenges

AI for Medical Image Security: A Comprehensive Review of Techniques and Challenges Benyoucef Aicha1and Hamadouche M’Hamed2 1Department of Electrical Systems Engineering, LIMOSE Laboratory, Faculty of Technology, [email protected] 2Department of Electrical Systems Engineering, LIMOSE Laboratory, Faculty of Technology, [email protected] Abstract Ensuring the security of sensitive medical data, including patient records and medical images, is paramount in the healthcare sector due to the risks of unauthorized access and data breaches. As healthcare information is increasingly transmitted through unsecured channels, maintaining its confidentiality, integrity, and authenticity is essential. This review examines AI-driven security techniques such as encryption, anomaly detection, and privacy-preserving algorithms, which play a crucial role in protecting medical data. By enhancing regulatory compliance and fostering trust in digital healthcare systems, these methods contribute significantly to data security. Additionally, this paper explores recent advancements in AI-based medical image protection and highlights key challenges and future research directions in the field of medical data security. Keywords: AI, Sybersecurity, medical image, cryptography, watermarking. 1 Introduction Given the critical sensitivity of healthcare data, it remains a primary target for cyberattacks, particularly as large volumes of information are accessed and transmitted over potentially unsecured networks. Ensuring data security requires robust protection measures at every stage, including storage, transmission, and retrieval. To safeguard patient information, researchers commonly employ techniques such as cryptography, steganography, and watermarking, which strengthen security and help prevent unauthorized access. [14,25] [7]. This review focuses on two primary types of health data—medical images and electronic health records (EHRs)—as these are commonly secured through encryption and watermarking. Medical images, derived from diagnostic tools like ultrasound and MRI, capture critical anatomical details and are essential for diagnosis and research, making their secure storage and transfer vital. EHRs, containing personal and medical information, are also crucial to protect, as they hold sensitive patient data. Securing medical images and EHRs is paramount to maintaining patient privacy, ensuring data integrity, and supporting trust in healthcare systems [10]. Security methods and techniques in the medical field help protect sensitive data, but with the rapid growth of Artificial intelligence (AI) applications in medical image segmentation and classification—particularly for enhancing diagnosis and cancer detection—AI has also become crucial for advancing medical data security [29]. AI is revolutionizing cybersecurity by enabling proactive threat detection and response through real-time data analysis and anomaly detection, enhancing systems like intrusion detection, malware analysis, and phishing detection while allowing security teams to focus on complex challenges [5]. The objective of this review is to examine recent AI-driven approaches to securing medical images, with a focus on key applications, methodologies, and emerging trends in the field. By analyzing current advancements, we aim to provide insights into how AI enhances medical image security and to identify areas for future research that could further strengthen privacy and data protection in healthcare. 2 Medical Image Security Threat Landscape The digital nature of the medical data and images exposes them to various cybersecurity threats. This section explores common security threats targeting medical images and their implications, highlighting the need for robust protective measures. 12 2.1 Types of Security Threats In July 2021, three organizations—Retinal Consultants Medical Group, ACE Surgical Supply, and Three Rivers Regional Commission—reported breaches in which unauthorized individuals accessed protected health information. These incidents affected a total of 25,725 patients, exposing personal data such as names, addresses, usernames, passwords, financial account numbers, and medical information, including treatment history and diagnoses. The compromised data posed significant risks, including identity theft, phishing attacks, and the potential alteration of medical records, which could lead to incorrect diagnoses and treatments [3]. 2.2 Vulnerabilities in healthcare This section explores the specific security challenges associated with the core components of e-health systems 2.2.1 Cloud computing platforms •Data Breaches: Data breaches in cloud services often occur due to poor security practices like weak passwords and the absence of multi-factor authentication, leading to the exposure of sensitive patient information [11]. •Unauthorized Access: Unauthorized access to cloud services often arises from misconfigurations and weak authentication protocols, which cybercriminals exploit through methods such as phishing [29], keylogging [30], person-in-the-middle (PITM) attacks, brute force attempts[33], and credential stuffing. These techniques enable attackers to steal or bypass login credentials, compromising sensitive data. 2.2.2 Internet of medical things (IOMT) The Internet of Medical Things (IoMT) enhances patient care through real-time data collection but poses significant security risks, including device vulnerabilities, data interception due to weak encryption, and susceptibility to remote attacks. These risks can compromise patient privacy, disrupt medical device functionality, and even endanger lives [35]. 2.2.3 Electronic health records (EHRS) Electronic Health Records (EHRs) are advanced digital systems that centralize and organize a wide array of patient information, including medical history, diagnoses, medications, immunization records, allergies, radiology images, and lab results. They provide real-time, patient-centered records that are instantly accessible to authorized personnel, anytime and anywhere. EHRs enhance collaboration by allowing multiple healthcare providers to share and access a patient’s information, enabling integrated care and better decision-making. They also improve workflows by reducing paperwork, increasing accuracy in record-keeping, and offering evidence-based tools to support clinical decisions. By centralizing and streamlining data, EHRs foster a more patient-centered approach, ensuring that care is tailored to individual needs. These features collectively make EHRs a cornerstone in modern healthcare systems, significantly contributing to better patient outcomes and operational efficiency [34,15]. 3 AI-Driven Techniques in Medical Image Security 3.1 Machine Learning-Based Encryption: 3.1.1 Securing Medical Image Analysis with Encryption Algorithms in Deep Learning Recent advancements in AI and encryption techniques are transforming healthcare by enabling secure and accurate medical data processing. Naik et al. [28] used DenseNet-121 and AES-128 encryption for identifying lung diseases from chest X-rays. Kumar et al. [21] implemented a cloud-based system for tumor detection in MRI images using CNN with 97.87% accuracy and AES-256 encryption. Mohanty et al. [26] achieved 98.51% accuracy in brain tumor detection with CNN-LSTM secured by a modified SHA-256 algorithm. Other method employed an LSTM model with homomorphic encryption for predicting in-hospital mortality using the MIMIC-III dataset. while [12] developed PINPOINT, a temporal 13 CNN with homomorphic encryption for time-series predictions, including COVID-19 case forecasting. In [27] reviewed homomorphic encryption applications in cancer detection, cardiovascular analysis, and secure healthcare queries. Boulila et al. [8] classified COVID-19 X-rays with MobileNetV2 and partially homomorphic encryption, achieving 93.3% accuracy. These innovations underscore the potential of combining AI with encryption for secure and efficient healthcare solutions. 3.1.2 Integrating Image Encryption and Compression in Deep Learning for Medical Image Processing The security and efficient transmission of medical images is essential due to their large size and sensitive nature. Several recent techniques address both encryption and compression to enhance protection. Selvi et al. [31] developed the ASFSCSLEC-DNL method for secure encryption and compression of chest radiograph images, producing promising results. Ahmad et al. [1] proposed a block-based perceptual encryption algorithm combined with JPEG compression for grayscale and color medical images, tested in TB screening on chest radiographs. Kumar et al. [20] introduced MediSecFed, a secure federated learning framework for chest X-ray datasets, outperforming FedAvg by 15% in hostile environments. Hajjaji et al. [13] proposed a novel crypto-compression algorithm using artificial neural networks and chaotic systems, which successfully preserved the security and quality of the medical image during compression. 3.1.3 Key Generation in Encryption Algorithms for Medical Image Analysis Key generation plays a crucial role in encryption algorithms for medical image analysis, ensuring the confidentiality, integrity, and authenticity of sensitive data. Ding et al. [18] proposed a deep learningbased key generation network (DeepKeyGen), which showed superior security to encrypt medical images, evaluated on data sets such as chest X-rays and the BraTS18 data set. Krishna et al. [19] introduced a dynamic medical image encryption technique using a neural network for key generation, encrypting the key itself for enhanced security. While their method demonstrated strong encryption, the encryption time needs optimization, as tested on X-ray images. 3.2 Watermarking and Data Integrity Verification: Current research on deep learning-based watermarking focuses mainly on image watermarking, with limited work on text and 3D images, offering improved efficiency and robustness by learning complex patterns resilient to attacks, easily re-trained for different applications, and making signature retrieval difficult due to high non-linearity [6,7]. Many methods in the literature presented CNN-based techniques for digital image watermarking that enhance both robustness and imperceptibility. These methods [32,2], [39,17] [22] utilize various CNN architectures, such as encoder-decoder networks and full convolutional neural networks (FCNNs), to efficiently embed and extract watermarks. They also introduce innovative strategies like adversarial training and attack simulation layers to improve resistance against distortions and attacks, ultimately achieving better trade-offs between robustness and imperceptibility. These CNN-based approaches outperform traditional methods, offering greater adaptability to different image resolutions and improving the overall security of the watermarking process. The second class of deep learning-based image watermarking utilizes generative adversarial networks (GANs), including variants like Wasserstein GANs (WGANs) and CycleGANs, known for their effectiveness in providing invisibility and robustness. HiDDeN [40] was the first scheme to use an adversarial discriminator to improve watermarking, featuring an encoder, decoder, and adversary network. ROMark [36] improved HiDDeN by minimizing the loss of decoding in various attacks, while another variant incorporated rotation and noise layers to defend against geometric rotations. Zhang et al. [38] introduced a GAN-based technique using inverse gradient attention (IGA) to improve capacity and robustness. Liu et al. [24] proposed a two-stage separable deep learning framework (TSDL), which trains with true nondifferentiable noise attacks like JPEG compression, achieving improved robustness compared to previous methods. 14 3.3 Privacy-preserving solutions in deep learning-based techniques Recent advances in secure medical data processing highlight the integration of security methods and deep learning to improve accuracy and privacy. Zhang et al. [37] optimized CryptoNets with polynomial ReLU approximations for better classification accuracy in networks with nonlinear layers, while Liu et al. [23] enhanced inference accuracy using MiniONN with secret sharing. Alzubi et al. [4] proposed a blockchainbased BAISMDT model for secure medical data transmission and disease detection. Hesamifard et al. [16] and Carpov et al. [9] emphasized reducing computational costs and improving security in encrypted systems through GPU batch bootstrapping and homomorphic encryption. Federated learning (FL) shows promise in real-world medical data exchange but faces challenges with noisy data, underscoring the need for further research into secure, efficient multiparty computation and privacy-preserving deep learning. 4 Evaluation and Benchmarking of AI Techniques Comparative Analysis: AI techniques in medical image security demonstrate varied performance across encryption strength, detection accuracy, and computational efficiency: •Encryption Strength: Techniques like homomorphic encryption (e.g., MiniONN, CryptoNets) and GAN-based frameworks (e.g., TSDL and IGA) excel in securing medical data during processing and transmission. Approaches integrating modified encryption algorithms, such as AES-128/256 or SHA-256, provide robust data protection, while blockchain-based models like BAISMDT enhance data privacy and integrity during exchange. •Detection Accuracy: CNN-based methods (e.g., DenseNet-121, CNN-LSTM) achieve high diagnostic accuracy, with some models reporting over 98% in medical image classification and tumor detection. GAN-based watermarking techniques also improve robustness and accuracy in image integrity checks. •Computational Efficiency: While encryption techniques like homomorphic encryption and neural network-based key generation offer strong security, they often face higher computational costs. Innovations like GPU acceleration, batch bootstrapping, and compression strategies reduce computational overhead, enabling practical deployment in real-world scenarios. Overall, integrating AI into medical image security balances high accuracy and robust encryption, though computational efficiency remains an area for further optimization. Dataset and Model Limitations: Medical image datasets face challenges of limited diversity, impacting the ability of AI models to generalize across various demographics, imaging technologies, and clinical settings. This lack of diversity hinders model robustness, particularly in ensuring the security of sensitive patient information during processing and transmission. While advancements like adversarial training, federated learning, and encryption-integrated models (e.g., homomorphic encryption, blockchain) improve data security and robustness, the reliance on biased or narrow datasets continues to limit the scalability and reliability of these AI solutions in real-world medical applications. 5 Challenges and Open Issues Deep learning for medical image security using cryptography or watermarking techniques faces various challenges such as •Limited Generalization Deep learning models often struggle to adapt to new or diverse medical image data, leading to decreased performance and security. Future research should focus on creating models that generalize well across various imaging modalities, diseases, and patient groups. •Vulnerability to Adversarial Attacks Adversarial attacks can manipulate input data, compromising the integrity and security of encrypted medical images. Future work should prioritize developing robust training techniques and protective mechanisms to mitigate such vulnerabilities. 15 •High Computational Costs One of the primary challenges in applying deep learning to medical image security is the high computational cost. Training deep learning models requires expensive hardware and extensive time. Future research could focus on optimizing algorithms and utilizing hardware accelerators like GPUs or TPUs to reduce these costs, enabling real-time, scalable solutions in healthcare applications. •Data Availability and Quality The scarcity of large, high-quality datasets due to privacy concerns poses a significant challenge. Future developments should focus on privacy-preserving techniques that enable model training on decentralized or encrypted datasets while maintaining data security. 6 Conclusion This paper explored various AI-driven techniques that play a crucial role in enhancing medical image security. Advanced methods such as convolutional neural networks (CNNs), generative adversarial networks (GANs), federated learning (FL), and homomorphic encryption (HE) have demonstrated remarkable effectiveness in strengthening encryption, improving threat detection, and optimizing computational efficiency. These approaches not only protect sensitive medical data but also ensure its integrity and accessibility within modern healthcare systems. AI has become indispensable in addressing the escalating cybersecurity challenges in healthcare. By enhancing data privacy and mitigating adversarial threats, AI-driven solutions bridge the gap between security demands and the rapid digital transformation of healthcare infrastructures. Their adaptability and scalability make them essential for managing the growing volumes of medical data securely. Looking ahead, the integration of AI presents vast opportunities for advancing medical data security. 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