Progress

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

Phase 1

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.

Phase 2

Established the spatial CNN baseline

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

Phase 3

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.

Phase 4Current

Analyzing fusion and deployment relevance

Fusion analysis now 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.

Phase 5

Introduced orchestration and reporting

Added agent-driven dataset discovery, delta-only processing, validation checks, and report generation. This work pushes BlinkTrace closer to a repeatable research loop that can eventually support production-style detection workflows.

Current

Sharpening the real-time story

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

What BlinkTrace demonstrates today

The project already shows evaluation maturity, vision-focused experimentation, and repeatable research operations. That combination matters to technical teams because it reflects the kind of judgment needed to move from offline experiments toward a real-time detection pipeline.

A public repository is coming soon. For now, this site focuses on the technical throughline and the most meaningful findings.