Detecting Deepfakes via Absence of Human Bio-Signals: A Conceptual Framework

Short summary

The article proposes detecting deepfakes by validating the presence of authentic human bio-signals—micro-expressions, pupil dilation, blood flow variations—rather than hunting for visual artifacts in fake media. It outlines a conceptual Python framework using autoencoders and isolation forests trained exclusively on real human physiological data to flag synthetic content by its absence of biological imperfections. The code is illustrative rather than production-ready, and the post is cut off before completing the implementation.

• •Deepfake detection shifts from spotting fakes to validating real human bio-signals like micro-expressions and pupil dynamics
• •Conceptual Python framework uses autoencoders and Isolation Forests for anomaly detection on authentic physiological data
• •Code is illustrative and incomplete—no working pipeline or real dataset is provided

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