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Securing Healthcare Data Integrity: Deepfake Detection Using Autonomous AI Approaches.

Chia-Chi Hsu, Min-Yan Tsai, Chia-Mu Yu

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A new dynamic deepfake detection framework, DProm, uses visual prompt tuning to adaptively safeguard healthcare data. This approach ensures robust detection of manipulated medical images and records in evolving smart healthcare systems.

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Area of Science:

Artificial Intelligence in Healthcare
Cybersecurity
Medical Imaging Analysis

Background:

Deepfake technology evolution presents significant risks to healthcare data integrity, including medical imaging and electronic health records (EHR).
The increasing integration of autonomous AI in smart healthcare necessitates advanced methods to counter deepfake misuse for data manipulation or impersonation.

Purpose of the Study:

To introduce DProm, a dynamic deepfake detection framework designed for adaptive and efficient security in healthcare AI systems.
To address the limitations of static detection models in adapting to novel deepfake techniques.

Main Methods:

DProm utilizes visual prompt tuning (VPT) with a pre-trained Swin Transformer for efficient adaptation to new data distributions.
The framework fine-tunes a small set of visual prompts, minimizing computational and storage overhead.

Main Results:

DProm achieved state-of-the-art performance in both static cross-dataset evaluations and dynamic scenarios.
The framework demonstrated robust detection capabilities across diverse and evolving data distributions.

Conclusions:

DProm offers a scalable, adaptable, and resource-efficient solution for deepfake detection in healthcare.
The framework enhances the security and trustworthiness of autonomous AI in smart healthcare applications, promoting safer medical data handling.