blinktrace · development record

From raw frames to a research system.

A technical timeline of how BlinkTrace moved from raw data processing to held-out evaluation, vision-focused experimentation, and deployment-minded research operations.

01 · milestones

  1. phase 1

    complete

    Built the preprocessing pipeline

    Established the path from raw video to frames, face crops, landmarks, normalized geometry, and sequence manifests. This created the data backbone needed for repeatable experimentation and future streaming-oriented workflows.

  2. phase 2

    complete

    Established the spatial CNN baseline

    Built an appearance-based detector around EfficientNet-B0 face crops and started evaluating it under held-out generation methods. The key lesson: benchmark-style performance means very little without stronger split discipline.

  3. phase 3

    complete

    Established the temporal GRU baseline

    Added landmark-sequence modeling with grouped, source-matched validation. This made it possible to test whether temporal facial dynamics add robust transfer signal beyond still-frame artifacts.

  4. phase 4

    in progress

    Analyzing fusion and deployment relevance

    Fusion analysis shows a nuanced result: combining visual branches can improve hard classification accuracy without necessarily improving ranking strength. That is directly relevant to real-time operating-point design.

  5. phase 5

    complete

    Introduced orchestration and reporting

    Added automation for dataset intake, delta-only preprocessing, validation checks, and report generation. This makes the visual pipeline repeatable and pushes BlinkTrace closer to production-style detection workflows.

  6. current

    next

    Sharpening the real-time story

    The current phase aligns the public presentation with the actual research: rigorous evaluation, deployment-minded experimentation, and a clear direction toward real-time deepfake detection.

02 · where things stand

What BlinkTrace demonstrates today

The project already shows evaluation maturity, vision-focused experimentation, and repeatable research operations — the kind of judgment needed to move from offline experiments toward a real-time detection pipeline.

What comes next

A public repository is coming soon, and the paper will be posted as a preprint on arXiv. For now, this site focuses on the technical throughline and the most meaningful findings.