Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections

One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device

Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos

Color-decoupled photo response non-uniformity for digital image forensics

The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification and authentication. However, the use of a colour filter array for capturing only one of the three colour components per pixel introduces colour interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colours obtained through the colour interpolation process is not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a Couple-Decoupled PRNU (CD-PRNU) extraction method, which first decomposes each colour channel into 4 sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification