Overview
A research project examining the capabilities and risks of AI-driven biometric data extraction from publicly available internet sources. Strong focus on ethical frameworks, privacy regulations, and responsible disclosure of findings.
Methodology
The system operates as a multi-stage OSINT pipeline. Stage one collects candidate imagery from social media platforms (Twitter, Instagram, LinkedIn), public government databases, and academic repositories via their public APIs and structured crawl strategies. Stage two runs an AI model cascade — MTCNN for face detection, DeepFace for facial landmark alignment and embedding extraction, and CLIP for cross-modal feature association. Stage three applies DBSCAN clustering on extracted embeddings to group images by identity across disparate sources. Each stage logs provenance metadata used downstream for audit and scoring.
Technical Stack
Core pipeline built in Python 3.11. Face detection and alignment use OpenCV with MTCNN backends. Feature extraction relies on DeepFace (VGGFace2 and Facenet512 backbones) and OpenAI CLIP for semantic linkage between visual features and textual context. Storage uses Elasticsearch with custom index mappings optimized for high-dimensional vector similarity search (cosine distance, IVF-PQ indexing). Orchestration via Celery with Redis as the message broker for distributed scraping jobs. The complete processing pipeline achieves ~120 images/min on a single NVIDIA A10G.
Legal Analysis
Biometric data falls under special category protection in most jurisdictions. GDPR Article 9 prohibits processing unless explicit consent or specific exemptions apply — the project maps each data source against these exemptions. KVKK (Turkey, Law No. 6698) mirrors GDPR’s special category regime with additional requirements for explicit consent in cross-border transfer. CCPA 2020 (California) does not currently classify biometric data as sensitive, though the CPRA amendments effective 2023 expand the definition of biometric information. A compliance matrix evaluates each collection pathway under all three frameworks concurrently, flagging jurisdictions where automated scraping may violate local processing bans or consent requirements.
Biometric Exposure Scoring
A composite scoring framework quantifies individual exposure across four dimensions: Breadth (number of sources containing identifiable imagery), Permanence (irreversibility — facial geometry vs. gait or voice), Quality (image resolution, pose variation, occlusion), and Context Leakage (associated metadata — geotags, timestamps, social graph edges). Each dimension is normalized to a 0–1 scale and aggregated into a weighted exposure index. This score enables risk-tiered alerting: scores above 0.75 trigger priority review for potential remediation recommendations.
Ethical Considerations
The project operates under a strict ethical charter. All collection targets only publicly accessible data — no authentication bypass, credential stuffing, or API abuse. Datasets are stored encrypted at rest with access logging; no raw images are published. Identified individuals are not re-identified in any published output. Responsible disclosure protocols are followed for any systemic vulnerabilities discovered in platform data-access controls. The research adheres to the Menlo Report principles (respect for persons, beneficence, justice, respect for law and public interest).
Impact
Highlights the growing tension between publicly available data and biometric privacy — contributing to the discourse on regulation and defensive measures against mass biometric harvesting.