Problem
Detect manipulated or falsified media using semantic evidence that extends beyond low-level image artifacts.
Context
The project combined object-level cues, scene text, and human-pose signals across manipulated content.
My role
Developed computer vision and semantic reasoning pipeline components.
Constraints
- Evidence comes from multiple imperfect signals.
- Evaluation must account for diverse manipulations and failure modes.
- Public descriptions must stay within approved research scope and avoid sensitive implementation details.
Architecture
The available source identifies object, text, and pose analysis feeding a semantic-forensics workflow.
Technical decisions
- Treated semantic evidence as multiple complementary signals.
- Used detection and structured visual reasoning components.
Trade-offs
Balancing multiple semantic cues requires attention to calibration, failure modes, explainability, and compute cost. The public summary intentionally keeps dataset and implementation details high-level.
Results
Contributed to semantic media-forensics research workflows focused on detecting inconsistencies across visual evidence streams.
Public note
Detailed diagrams and qualitative examples are kept out of the public portfolio when release scope is limited.
Related links
Related public references can be listed as they become available.