Author Archives: Marco Fontani

PRNU-based Camera Identification in Amped Authenticate

Source device identification is a key task in digital image investigation. The goal is to link a digital image to the specific device that captured it, just like they do with bullets fired by a specific gun (indeed, image source device identification is also known as “image ballistics”).

The analysis of Photo Response Non-Uniformity (PRNU) noise is considered the prominent approach to accomplish this task. PRNU is a specific kind of noise introduced by the CMOS/CCD sensor of the camera and is considered to be unique to each sensor. Being a multiplicative noise, it cannot be effectively eliminated through internal processing, so it remains hidden in pixels, even after JPEG compression.

In order to test if an image comes from a given camera, first, we need to estimate the Camera Reference Pattern (CRP), characterizing the device. This is done by extracting the PRNU noise from many images captured by the camera and “averaging” it (let’s not dive too deep into the details). The reason for using several images is to get a more reliable estimate of the CRP, since separating PRNU noise from image content is not a trivial task, and we want to retain PRNU noise only.

After the CRP is computed and stored, we can extract the PRNU noise from a test image and “compare” it to the CRP: if the resulting value is over a given threshold, we say the image is compatible with the camera.

Camera identification through PRNU analysis has been part of Amped Authenticate for quite some time. However, many of our users told us that the filter was hard to configure, and results were not easy to interpret. So, since the end of last year, a new implementation of the algorithm was added (Authenticate Build 8782). The new features included:

Advanced image pre-processing during training
In order to lower false alarms probability, we implemented new filtering algorithms to remove artifacts that are not discriminative, something that is common with most digital cameras (e.g., artifacts due to Color Filter Array demosaicking interpolation).

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Amped at the GTTI 2017 Thematic Meeting on Multimedia Signal Processing

Today we presented our products Amped FIVE and Amped Authenticate at the GTTI 2017 Thematic Meeting on Multimedia Signal Processing. The meeting gathered 50+ people from Italian universities and research labs. We got lots of attention and many hints that will help improve our products in the future – stay tuned!

AMPED Software at the DEMO session of the GTTI-MMSP meeting

 

 

Digging out the processing history of an image with the Correlation Plot

In the recent post detailing the latest release of Amped Authenticate, 7644, we highlighted the new functionality included in the Correlation Plot.

When it comes to investigating a digital image, it is fundamental to understand as much as possible about its processing history. The more we know about the digital history of the image, the better we can design our way towards authentication. For example, we can properly choose which filters make sense to use and which not. Not only that, we may also find that some processing was carried that seriously undermine the integrity of the image, or gather evidence supporting authenticity of the image.

The Correlation Plot is a powerful tool in Amped Authenticate for detecting traces left by many different processing operations. It has been part of Authenticate for some time, but we recently worked hard to make its output easier to interpret and understand.

Before looking at the enhancements, what does the correlation plot do?

Image 4

The Correlation Plot, with some new additions!

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