Camera Fingerprint PRNU
Extract the unique sensor noise fingerprint from a photo. Compare two images to determine if they were taken by the same camera. Based on Photo Response Non-Uniformity analysis.
500+ sensor fingerprints extracted
Drag & drop a file here, Ctrl+V to paste, oror
Sensor Fingerprint
Every camera sensor has manufacturing imperfections that create a unique fixed-pattern noise — like a fingerprint. PRNU (Photo Response Non-Uniformity) captures this signature from any photo taken by the sensor.
Two-Image Comparison
Upload a second image to compare sensor fingerprints. High correlation (NCC) means both images likely came from the same physical camera. Used in digital forensics to link photos to specific devices.
Privacy-First
All PRNU extraction and comparison runs in your browser via Web Workers. Images and noise patterns never leave your device. No server processing, no data retention, no accounts required.
What is PRNU camera fingerprinting?
Every camera sensor has tiny manufacturing imperfections that produce a unique fixed-pattern noise called Photo Response Non-Uniformity (PRNU). This noise is imperceptible to the eye but embedded in every photo the sensor captures — acting as a hardware fingerprint. By extracting and comparing this pattern, forensic analysts can link photos to specific devices. For metadata-level camera identification, check the EXIF Checker or Noise Analysis tool.
Two-image sensor matching
Upload two photos and this tool extracts the PRNU noise from each, then computes a normalized cross-correlation (NCC) score. High correlation means both images likely came from the same physical sensor. This technique is used in law enforcement to link evidence photos to seized devices. For visual similarity comparison without sensor analysis, use the Image Comparator or Similarity Scanner.
Accuracy and limitations
PRNU works best on high-resolution, lightly compressed images. Heavy JPEG compression, aggressive noise reduction, or heavy editing can degrade the fingerprint. Cropped or resized images lose spatial alignment, reducing correlation accuracy. Screenshots and AI-generated images lack sensor noise entirely. For broader manipulation detection, combine with ELA, CFA Pattern Analysis, and the Authenticity Checker.
PRNU in the forensic toolkit
PRNU identifies the source device. Shadow Consistency checks lighting physics. Clone Detection finds copy-paste regions. Together they cover hardware, physics, and pixel-level forgery signals. Explore all 88 tools at Scanly's full toolkit.
PRNU extraction and comparison run entirely in your browser. Your images and noise patterns never leave your device. Download the noise visualization as PNG for forensic reports.
Camera sensor fingerprinting has been used in criminal investigations since the mid-2000s, and academic research consistently shows PRNU as the most reliable method for linking a photograph to a specific device. Unlike metadata-based identification (which can be edited or stripped), PRNU is a physical property of the sensor hardware that cannot be removed from the image without destroying the image itself. This makes it valuable for digital forensics, insurance fraud investigation, and intellectual property disputes where proving which camera captured a specific image is critical.
For a complete image verification workflow, start with the EXIF Checker to review metadata, then use the Authenticity Checker for multi-signal analysis, and finish with PRNU comparison for hardware-level confirmation. The FFT Spectrum Analyzer can also reveal frequency-domain artifacts that complement PRNU findings. Learn more in our articles on photo integrity for digital evidence and complete forensics guide.