Proving Reality: Why Sensor Data Provenance Matters
AI systems depend on sensors to perceive the world, but what happens when those sensors lie?
When a lidar frame or camera image can be edited, every autonomous decision becomes a risk.
That’s why I’ve been exploring sensor data provenance, building systems that prove what your sensors actually saw, and when they saw it.
The Problem: When Sensors Lie
Robots and vehicles collect terabytes of data every day, but most of it can be silently altered.
A single changed pixel or missing log entry can distort ground truth and invalidate an entire test.
Without traceable data integrity, accountability disappears, both in the lab and in the field.
The Concept: Cryptographic Provenance Chains
Provenance turns every sensor event into a cryptographic statement of truth.
Each frame or reading is:
Hashed using SHA-256
Timestamped at acquisition
Linked to the previous hash
This creates a tamper-evident chain, where modifying one entry breaks the sequence, instantly exposing corruption.
It’s like a local, efficient blockchain for sensor trust.
The Build: Jetson Orin Nano Dev Kit
To test the idea, I built an open prototype:
hash-chaining-camera on GitHub
The project captures camera frames, generates hashes, chains them together, and writes verifiable logs.
It runs on compact hardware like the Jetson Orin Nano or Raspberry Pi, proving that edge provenance doesn’t require massive infrastructure.
The Impact: Trust at the Edge
Provenance transforms raw data into auditable truth.
For autonomous systems, it means crash investigations backed by cryptographic certainty.
For robotics or drones research, it means reproducibility you can verify mathematically.
And for engineers, it means never wondering again whether a dataset has been “cleaned” too much.
Closing
Sensor data provenance isn’t about paranoia, it’s about preserving trust in motion.
As AI agents and autonomous systems take on much more responsibility, provenance will be their digital conscience of truth.
