Modern medical devices integrate hardware and software, thereby expanding the attack surface and creating patient safety risks. Regulators set cybersecurity expectations, and international standards guide implementation. However, outcome-based, method-agnostic policies give manufacturers wide latitude, producing variability, ambiguity, and fragmented, non-reproducible practices in training and education. We address this gap with a reproducible, open-source reference platform that operationalizes security-by-design in alignment with current regulatory expectations and standards. We also report an initial pilot course using the platform as a foundation for consistent medical-device cybersecurity curricula.

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under attack.Our method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions.

The magnetotelluric (MT) inversion technology is crucial for quantitatively interpreting deep mineral resources, especially when combined with rock physics information, enhancing accuracy in assessing underground structural parameters and spatial distribution. However, the traditional fuzzy c-means (FCMs) clustering-constrained inversion method requires prior rock physics information for each geological unit, limiting their application scope. We propose a guided FCMs (GFCMs) clustering-constrained inversion method based on adaptive virtual rock physics information, referred to as XG-FCM-constrained MT inversion. This method breaks free from the constraints of traditional methods by not relying on prior rock physics information. In terms of extracting virtual rock physics information, we employ a local density clustering algorithm to dynamically extract resistivity model information from MT inversion iterations, automatically determining the number of clusters and cluster centers. Regarding the inversion strategy, we construct an integrated objective function that combines data fitting, smoothing constraint, and GFCM constraint, implementing a two-stage iterative solution strategy of “smoothing first, clustering second.” Model testing demonstrates that compared to traditional smoothing-constrained MT inversion, the XG-FCM-constrained method achieves a significant improvement in the resolution of resistivity model reconstruction, clearly delineating the boundaries of underground anomalies. Even in situations where rock physics information is insufficient or absent, this method can effectively reconstruct high-quality underground resistivity models, reducing the dependence on complete prior information. The application of actual field data further highlights the advantages of the XG-FCM-constrained MT inversion method, providing robust support for accurately delineating geological unit boundaries and precisely identifying potential ore deposit target areas.