Deepfake and biomedical data manipulation: an emerging threat

The convergence of advanced artificial intelligence and biomedical data has opened up a wide range of revolutionary possibilities in medicine and research. However, it has also introduced emerging risks that demand immediate attention from a cybersecurity perspective. Among these risks, the use of Deepfake technologies and the manipulation of biomedical data represent a growing threat, with critical implications for information integrity, trust in healthcare systems, and patient safety.

What are deepfakes and how do they relate to biomedical data?

The term Deepfake refers to techniques based on neural networks, primarily Generative Adversarial Networks (GANs), capable of generating highly realistic fake images, videos, or audio. While they initially gained attention in entertainment and political disinformation, their potential to manipulate biomedical data poses significant risks.

Risks associated with the manipulation of biomedical data

Integrity of electronic medical records

Hospital information systems and electronic medical records (EMRs) rely on accurate data to ensure correct diagnoses and effective treatments. The introduction of manipulated data through Deepfake techniques can compromise this integrity. A concrete example is the alteration of laboratory images or diagnostic imaging studies, which could lead to the prescription of incorrect treatments, putting patients’ lives at risk.

Fraud in clinical and pharmaceutical research

In clinical research, medical trials depend on large volumes of accurate and verifiable data. Manipulating MRI images, genetic test results, or biomarkers can be used to falsify study outcomes or fraudulently accelerate drug approvals. This type of threat not only has financial implications for pharmaceutical companies but also carries ethical and legal consequences, undermining public trust in biomedical research.

Threats to patient privacy and security

Techniques for manipulating and detecting biomedical deepfakes

The manipulation of biomedical data using Deepfakes primarily relies on deep learning algorithms. GANs can be trained with medical image datasets to generate highly convincing fake replicas. Additionally, image-to-image translation techniques allow modification of existing images to introduce nonexistent findings or remove critical anomalies. Another emerging technique is biomedical voice synthesis, which could be used to falsify medical dictations or clinical notes in voice-recognition systems.

Detecting biomedical Deepfakes requires a multidimensional approach. Some methods focus on the statistical consistency of pixels and noise patterns in images, while others employ specialized neural networks trained to identify irregularities in textures and edges. Verification of metadata and traceability of the original records are also critical components of a robust detection strategy. However, the constant evolution of generation algorithms makes this task extremely challenging, making proactive prevention and monitoring essential components of cybersecurity.

Legal consequences

Manipulating biomedical data with Deepfakes is not only a technical risk but also a legal and ethical challenge. From a regulatory perspective, legislation such as the GDPR in Europe mandates strict protection of personal and biometric data, imposing significant responsibilities on hospitals, laboratories, and medical technology companies. In many countries, falsifying medical records is considered a serious crime, but the advent of Deepfakes introduces a level of sophistication that complicates the identification of those responsible.

Cybersecurity measures to mitigate this type of threat

Implementation of advanced verification systems

One of the most effective strategies is the deployment of systems that integrate cryptographic verification of biomedical data. Digital signatures, blockchain, and record traceability techniques help ensure that information has not been altered since its origin.

Staff awareness and training

The human factor remains a critical link in biomedical cybersecurity. Continuous training of doctors, technicians, and administrative personnel in anomaly detection and digital security protocols is key to minimizing risks associated with Deepfakes.

Regular cybersecurity audits and forensic analysis