DICOM Compatible, 3D Multimodality Image Encryption using Hyperchaotic Signal

TL;DR

This work tackles secure handling of DICOM-based medical images across multiple modalities by introducing a hyperchaotic, multi-level diffusion encryption pipeline augmented with CAPTCHA-based authentication. The method combines pixel permutation, 2-, 4-, and 8-bit diffusion, plus adjacent and radial diffusion, driven by extreme hyperchaotic signals, and extends to 3D models with whole- or ROI-based encryption. A dual-layer security scheme is achieved by superimposing an encrypted CAPTCHA image on the encrypted medical image, with decryption mirroring all steps to recover the original data and verify authentication. Security analyses across histograms, entropy, correlations, anomaly detection, and differential attacks demonstrate high randomness, strong decorrelation, and robust resistance to various cryptanalytic threats, supporting secure storage and transmission in telemedicine, cloud, and IoT contexts.

Abstract

Medical image encryption plays an important role in protecting sensitive health information from cyberattacks and unauthorized access. In this paper, we introduce a secure and robust encryption scheme that is multi-modality compatible and works with MRI, CT, X-Ray and Ultrasound images for different anatomical region of interest. The method utilizes hyperchaotic signals and multi-level diffusion methods. The encryption starts by taking DICOM image as input, then padding to increase the image area. Chaotic signals are produced by a logistic map and are used to carry out pixel random permutation. Then, multi-level diffusion is carried out by 4-bit, 8-bit, radial and adjacent diffusion to provide high randomness and immunity against statistical attacks. In addition, we propose a captcha-based authentication scheme to further improve security. An algorithm generates alphanumeric captcha-based image which is encrypted with the same chaotic and diffusion methods as the medical image. Both encrypted images(DICOM image and captcha image) are then superimposed to create a final encrypted output, essentially integrating dual-layer security. Upon decryption, the superimposed image is again decomposed back to original medical and captcha images, and inverse operations are performed to obtain the original unencrypted data. Experimental results show that the proposed method provides strong protection with no loss in image integrity, thereby reducing unauthorized data breaches to a significant level. The dual-encryption approach not only protects the confidentiality of the medical images but also enhances authentication by incorporating captcha.

Figures (17)

• Figure 1: Lyapunov exponent spectrum and one-parameter bifurcation diagram of 3D hyperchaotic map. The one-parameter Bifurcation diagram of a 3D hyperchaotic map is illustrated in Figure (a). The structural diagram of the above-mentioned 3D hyperchaotic map is provided in Figure (b), with the Lyapunov exponent.
• Figure 2: A two parameter $a_1$-$b_2$ Lyapunov chart colored based on the legend for various attractors. The parameters are fixed as $a_2$=0.25,$a_3$=0.12,$b_1$=4,$c$=2.15
• Figure 3: Flowchart showing every stage of the image encryption process, from obtaining input to producing a safe encrypted output. This guarantees that data protection procedures are visualized clearly.
• Figure 4: Flowchart showing how the dicom file is being handled throughout the encryption and decryption processes.
• Figure 5: Zero-padding is the used to add additional rows and columns with zeros around an image's boundaries. By doing this, the picture size is maintained during processes like convolution.