Abstract
Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image's noise residual with the device's reference PRNU is often compared with a decision threshold to check the existence of the PRNU. A PRNU correlation predictor is usually used to determine this decision threshold assuming the correlation is content-dependent. However, we found that not only the correlation is content-dependent, but it also depends on the camera sensitivity setting. Camera sensitivity, commonly known by the name of ISO speed, is an important attribute in digital photography. In this work, we will show the PRNU correlation's dependency on ISO speed. Due to such dependency, we postulate that a correlation predictor is ISO speed-specific, i.e. reliable correlation predictions can only be made when a correlation predictor is trained with images of similar ISO speeds to the image in question. We report the experiments we conducted to validate the postulate. It is realized that in the real-world, information about the ISO speed may not be available in the metadata to facilitate the implementation of our postulate in the correlation prediction process. We hence propose a method called Content-based Inference of ISO Speeds (CINFISOS) to infer the ISO speed from the image content.
Highlights
W HEN a digital image is used in a forensic investigation or presented as evidence to the court, it is important to authenticate the image to ensure its content is free from manipulation
Recognizing that in the realworld, information about the ISO speed may not be available to facilitate the implementation of our postulate in the correlation prediction process, we propose a method called Content-based Inference of ISO Speeds (CINFISOS, /’sin.f@.s@s/) in Section IV to infer the ISO speed from the image content
Notice that when we introduce Photo Response Non-Uniformity (PRNU) by considering different photo-electron conversion rate, ηi, at each pixel to the raw pixel intensity model from [26], the noise residual variance model described in Equation (10) becomes a quadratic function of the expected pixel intensity φ, which can be expressed as: σres2 = Aφ2 + Bφ + C
Summary
W HEN a digital image is used in a forensic investigation or presented as evidence to the court, it is important to authenticate the image to ensure its content is free from manipulation. Many different algorithms have been proposed for PRNU-based source camera identification [1]–[11] and image forgery detection [12]–[17]. In most of these works, PRNU is utilized by computing the image-wise or block-wise correlations between the source device’s reference PRNU and the test image’s PRNU. These images allow us to conduct studies on the ISO speed’s influence on the correlation
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