Take a cell image dataset as initial ground truth.
Data Engineering Case Study
Scientific Image Forgery Detection Pipeline
End-to-end pipeline for a Kaggle competition: synthetic image generation, dataset versioning and publication via Kaggle API, automated training, model export, and submission delivery.
Top 10 leaderboard (1 month)
API-driven dataset versioning
Automated train/export/submit
Data Engineering Impact
The objective was not only model score. The project was engineered as a reusable and reliable data workflow that can be rerun with low manual effort and clear traceability.
- Abilities acquired: synthetic data generation, API dataset versioning, workflow orchestration, and reproducible model delivery.
- Success: reached top 10 on the competition leaderboard during one month.
- Pipeline ownership: from source data to published dataset, training run, artifact export, and final prediction submission.
Forged Image Examples
Original sample 01
Forged sample 01
Original sample 02
Forged sample 02
Pipeline Schema
Apply transformation logic to create new forged images.
Create and push a new dataset version using Kaggle API.
Use the generated dataset in the training notebook pipeline.
Export model outputs and artifacts through Kaggle workflow.
Run inference and submit predictions to the competition board.