PRNU Analysis

PRNU (Photo Response Non-Uniformity) analysis examines unique sensor noise patterns that act like a fingerprint for real cameras. AI-generated images lack these authentic sensor patterns.

Every camera sensor has manufacturing imperfections creating unique noise patterns. AI generators cannot replicate these authentic hardware signatures, making their absence a strong indicator of synthetic content.

We use wavelet denoising to separate the image content from the noise layer. The noise residual is then analyzed for statistical properties characteristic of real sensor noise.

Yes, PRNU patterns persist through moderate JPEG compression (quality 50+). Heavy compression or multiple re-compressions can degrade the signal but rarely eliminate it completely.

Current AI generators do not replicate authentic PRNU. Even if synthetic noise is added, it lacks the statistical properties and spatial correlations of real sensor noise.

Real photos typically have noise variance between 0.001-0.01. AI images often show unnaturally low variance (<0.0005) or synthetic uniform noise patterns.

Light editing preserves PRNU. Heavy filtering, resizing, or noise reduction can weaken it. This is why we use PRNU alongside other detection methods in our ensemble.

PRNU analysis achieves 75-85% accuracy as a standalone method. Its value increases significantly when combined with other detection techniques in our ensemble approach.

Smartphone cameras have distinct PRNU patterns. Computational photography may alter these patterns somewhat, but genuine sensor noise characteristics typically remain detectable.

PRNU provides strong evidence of camera origin but can be affected by post-processing. The 15% weight balances its reliability with methods less sensitive to image modifications.