Digital Camera Identification From Sensor Pattern Noise
Abstract
In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.
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PRNU constitutes one of the most direct and physically interpretable forms of evidence in media forensics, enabling conclusions such as a given region was not captured by the same device as the rest of the image.
“PRNUconstitutes one of the most direct and physically interpretable forms of evidence in media forensics, enabling conclusions such as the following: a given region was not captured by the same device as the rest of the image.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [9]
PRNU pattern is inherent to the sensor and remains consistent for authentic images.
“This PRNU pattern is inherent to the sensor and remains consistent for authentic images.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [9]
Every digital camera sensor possesses unique microscopic manufacturing imperfections, which produce a stable and distinctive noise pattern across images.
“The underlying principle is that every digital camera sensor possesses unique microscopic manufacturing imperfections, which produce a stable and distinctive noise pattern across images.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [9]
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“The noise-based Deepfake detection approaches up to date are mostly relying on the Photo Response Non-Uniformity (PRNU), a noise pattern created by small factory defects in the light-sensitive sensors of a digital camera (Lukas, Fridrich, and Goljan 2006).”
MethodBlind PRNU-Based Image Clustering for Source Identification
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From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics
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“The seminal work by Lukas et al. [9] systematically introduced the use of sensor pattern noise, particularly PRNU, as a unique identifier for the source camera.”
- Authors
- Luka, J., Fridrich, J., & Goljan, M.
- Affiliations
- Binghamton University (US)
- ORCID
- Fridrich, J.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2006-06
- Volume
- 1
- Issue
- 2
- Pages
- 205-214
- ISSN
- 1556-6013 (ISSN-L)
- OpenAlex
- W2124695272
- Cited by
- 1,328
- References
- 0
- Citations by year
- 2026: 62 · 2025: 67 · 2024: 54 · 2023: 92 · 2022: 92 · 2021: 78 · 2020: 102 · 2019: 96 · 2018: 94 · 2017: 73 · 2016: 68 · 2015: 91 · 2014: 63 · 2013: 54 · 2012: 53
- FWCI
- 37.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- sensor pattern noise, camera fingerprinting, spread spectrum watermarking, correlation detection, image denoising, false alarm rate, false rejection rate, JPEG compression, gamma correction, consumer digital cameras
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