Analysis of Sensor Photo Response Non-Uniformity in RAW Images

Knight, Simon; Moschou, Simon; Sorell, Matthew

doi:10.1007/978-3-642-02312-5_15

Cited by 7 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dark grey boxes indicate sources of noise that can be easily removed. Random processes are traditionally removed through frame averaging [12] while RAW format images remove digital processing artefacts [15]. The low-frequency components of the scene content and all other sources of noise are removed due to the high-pass filter that the images are passed through to obtain the noise residue in the current unique PRNU signal fingerprinting method .…”

Section: Rethinking the Noise Model For Forensic Advantagementioning

confidence: 99%

Rethinking Image Sensor Noise for Forensic Advantage

Matthews,

Sorell,

Falkner

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Sensor pattern noise has been found to be a reliable tool for providing information relating to the provenance of an image. Conventionally sensor pattern noise is modelled as a mutual interaction of pixel non-uniformity noise and dark current. By using a wavelet denoising filter it is possible to isolate a unique signal within a sensor caused by the way the silicon reacts non-uniformly to light. This signal is often referred to as a fingerprint. To obtain the estimate of this photo response non-uniformity multiple sample images are averaged and filtered to derive a noise residue. This process and model, while useful at providing insight into an images provenance, fails to take into account additional sources of noise that are obtained during this process. These other sources of noise include digital processing artefacts collectively known as camera noise, image compression artefacts, lens artefacts, and image content. By analysing the diversity of sources of noise remaining within the noise residue, we show that further insight is possible within a unified sensor pattern noise concept which opens the field to approaches for obtaining fingerprints utilising fewer resources with comparable performance to existing methods.

show abstract

Section: Rethinking the Noise Model For Forensic Advantagementioning

confidence: 99%

Rethinking Image Sensor Noise for Forensic Advantage

Matthews,

Sorell,

Falkner

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There still remains questions regarding the science of the method due to high-frequency components of the image remaining within either the image fingerprint, the camera reference pattern or both. These high frequency components include but are not limited to image compression artefacts [2], dark current [8], amplifier noise [8], and lens and optical effects [3] including dust [5]. These high frequency components can corrupt the fingerprint if their energy dominates the unique signal and are significantly uncorrelated to the sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The dark grey boxes indicate sources of noise that can be easily mitigated. Random processes are mitigated through frame averaging [8] while RAW format images remove digital processing artefacts [3]. Camera Noises introduced through computer algorithms such as demosaicking, dynamic range adjustments and downsampling are controlled through correct choice of camera hardware values prior to imaging [8].…”

Section: Introductionmentioning

confidence: 99%

“…[1] using [8] identified that the only sources of noise not reduced through frame averaging were dark current and PRNU. This was further refined in [3] to eliminate compression level artefacts through the use of raw images. LOS aberrations and components of the scene remain ( Figure 3).…”

Section: Introductionmentioning

confidence: 99%

“…Much work has been done to solve the open and closed set camera identification problem [1,2,3,4,5]. One of the most promising methods used to identify an image uniquely to not just a particular make or model of camera but the unique image sensor itself is that of photo-response non-uniformity noise (PRNU) or sensor pattern noise (SPN) [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An analysis of optical contributions to a photo-sensor's ballistic fingerprints

Matthews

Sorell

Falkner

2019

Digital Investigation

View full text Add to dashboard Cite

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Lukáš, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature.In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.

show abstract

Factors Influencing the Adoption of Using Mobile Banking on a Smartphone: an Empirical Case Study in Bangkok, Thailand

Suanmali

2015

Communications in Computer and Information Science

View full text Add to dashboard Cite

Analysis of Sensor Photo Response Non-Uniformity in RAW Images

Cited by 7 publications

References 7 publications

Rethinking Image Sensor Noise for Forensic Advantage

Rethinking Image Sensor Noise for Forensic Advantage

An analysis of optical contributions to a photo-sensor's ballistic fingerprints

Factors Influencing the Adoption of Using Mobile Banking on a Smartphone: an Empirical Case Study in Bangkok, Thailand

Contact Info

Product

Resources

About