Anti-Fraud Mechanisms

User-generated perception images represent the most open and flexible contribution channel, and therefore require the strongest fraud protections. The Robotin backend applies layered validation covering content authenticity, capture validity, user behavior consistency, and identity integrity.

AI-Generated and Manipulated Image Detection

The system detects:

• generative model fingerprints

• synthetic texture patterns

• inconsistent global illumination

• pixel-level editing artifacts

• EXIF and content mismatches

Images created via AI tools or altered for reward farming are rejected.

Duplicate and Spam Submission Filtering

Robotin employs:

• perceptual hashes

• deep similarity embeddings

• multi-view scene comparison

• contributor history correlation

Repetitive or near-duplicate submissions—even with slight modifications—are filtered out.

Metadata and Environmental Consistency Checks

All images undergo contextual verification, including:

• timestamp plausibility

• resolution and device metadata consistency

• household scene continuity analysis

• detection of internet-sourced or previously published images

This prevents plagiarism and mass-reused content.

Capture Liveness Validation

To confirm that images reflect real, in-situ household conditions, the system evaluates:

• natural hand-held motion characteristics

• angle and viewpoint variance

• ambient light patterns

• device noise signatures

This ensures contributions originate from genuine user actions.