Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification

Photo Response Non-Uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for source camera identification and image authentication. The abundant information that the sensor pattern noise carries in terms of the frequency content makes it unique, and hence suitable for identifying the source camera and detecting image forgeries. However, the PRNU extraction process is inevitably faced with the presence of image-dependent information as well as other non-unique noise components. To reduce such undesirable effects, researchers have developed a number of techniques in different stages of the process, i.e., the filtering stage, the estimation stage, and the post-estimation stage. In this paper, we present a new PRNU-based source camera identification and verification system and propose enhancements in different stages. First, an improved version of the Locally Adaptive Discrete Cosine Transform (LADCT) filter is proposed in the filtering stage. In the estimation stage, a new Weighted Averaging (WA) technique is presented. The post-estimation stage consists of concatenating the PRNUs estimated from color planes in order to exploit the presence of physical PRNU components in different channels. Experimental results on two image datasets acquired by various camera devices have shown a significant gain obtained with the proposed enhancements in each stage as well as the superiority of the overall system over related state-of-the-art systems

Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for image authentication and source camera identification. The abundant information that the PRNU carries in terms of the frequency content makes it unique, and therefore suitable for identifying the source camera and detecting forgeries in digital images. However, PRNU estimation from smartphone videos is a challenging process due to the presence of frame-dependent information (very dark/very textured), as well as other non-unique noise components and distortions due to lossy compression. In this paper, we propose an approach that considers only the non-textured frames in estimating the PRNU because its estimation in highly textured images has been proven to be inaccurate in image forensics. Furthermore, lossy compression distortions tend to affect mainly the textured and high activity regions and consequently weakens the presence of the PRNU in such areas. The proposed technique uses a number of texture measures obtained from the Grey Level Cooccurrence Matrix (GLCM) prior to an unsupervised learning process that splits the feature space through training video frames into two different sub-spaces, i.e., the textured space and the non-textured space. Non-textured video frames are filtered out and used for estimating the PRNU. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art approach

Three Dimensional Denoising Filter For Effective Source Smartphone Video Identification and Verification

The field of digital image and video forensics has recently seen significant advances and has attracted attention from a growing number of researchers given the availability of imaging functionalities in most current multimedia devices at no cost including smartphones and tablets. Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. However, estimating the PRNU from smartphone videos can be a challenging process because of the lossy compression that digital videos normally undergo for various reasons in addition to other non-unique noise components that interfere with the video data. This paper presents a new filtering technique for PRNU estimation based on the three-dimensional discrete wavelet transform followed by a 3D wiener filter. The rationale is that the 3D filter can filter out the compression artifacts along the temporal dimension in a more effective way than simple averaging. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the well-known two-dimensional wavelet-based Wiener approach

PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one

A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video

Digital camera identification using sensor pattern noise for forensics applications

Nowadays, millions of pictures are shared through the internet without applying any authentication system. This may cause serious problems, particularly in situations where the digital image is an important component of the decision making process for example, child pornography and movie piracy. Motivated by this, the present research investigates the performance of estimating Photo Response Non-Uniformity (PRNU) and developing new estimation approaches to improve the performance of digital source camera identification. The PRNU noise is a sensor pattern noise characterizing the imaging device. Nonetheless, the PRNU estimation procedure is faced with the presence of image-dependent information as well as other non-unique noise components. This thesis primarily focuses on efficiently estimating the physical PRNU components during different stages. First, an image sharpening technique is proposed as a pre-processing approach for source camera identification. The sharpening method aims to amplify the PRNU components for better estimation. In the estimation stage, a new weighted averaging (WA) technique is presented. Most existing PRNU techniques estimate PRNU using the constant averaging of residue signals extracted from a set of images. However, treating all residue signals equally through constant averaging is optimal only if they carry undesirable noise of the same variance. Moreover, an improved version of the locally adaptive discrete cosine transform (LADCT) filter is proposed in the filtering stage to reduce the effect of scene details on noise residues. Finally, the post-estimation stage consists of combining the PRNU estimated from each colour plane aims to reduce the effect of colour interpolation and increasing the amount of physical PRNU components. The aforementioned techniques have been assessed on two image datasets acquired by several camera devices. Experimental results have shown a significant improvement obtained with the proposed enhancements over related state-of-the-art systems. Nevertheless, in this thesis the experiments are not including images taken with various acquisition different resolutions to evaluate the effect of these settings on PRNU performance. Moreover, images captured by scanners, cell phones can be included for a more comprehensive work. Another limitation is that investigating how the improvement may change with JPEG compression or gamma correction. Additionally, the proposed methods have not been considered in cases of geometrical processing, for instance cropping or resizing

Weighted averaging-based sensor pattern noise estimation for source camera identification

Sensor pattern noise has been broadly used in the literature for image authentication and source camera identification. The abundant information that a sensor pattern noise carries in terms of the frequency content makes it unique and hence suitable for source camera identification. The traditional approach for estimating the sensor pattern noise uses a set of images to estimate a pattern residual signal from each image. The estimated residual signals are then averaged to obtain the sensor pattern noise. This is based on the assumption that each residual signal is a noisy observation of the sensor pattern noise. Such an assumption is well justified in practice because the images are acquired under different conditions making the corresponding residual signals distinct from each other. For instance bright images provide better sensor pattern noise estimation than dark images. Also, saturated pixels cause undesirable noise in residual signals. Inspired by this observation, a weighted averaging approach is proposed for efficient sensor pattern noise estimation. The proposed approach has been validated with two sensor pattern noise estimation techniques from the literature and significant improvements have been shown through experimental results

Image Sharpening for Efficient Source Camera Identification Based on Sensor Pattern Noise Estimation

Sensor pattern noise (SPN) has been widely used for image authentication and camera source identification. Its abundance in terms of the information that it carries along a wide frequency range allows for reliable identification in the presence of many imaging sensors. SPN estimation relies on the difference between a set of images and their smoothened versions to capture the characteristics of the sensor. Therefore, this process uses a part of the sensor noise content which is concentrated in the high frequency range and present in edges, contours and textured areas of the images. In this report, we propose to use a sharpening method to amplify the PRNU components for better estimation, thus enhancing the performance of camera source identification (CSI). Significant improvements have been achieved by the proposed method as demonstrated with two recent source camera identification techniques

Modality identification for heterogeneous face recognition

Identifying the type of modalities of the query image which can be of types visual, NIR, digital camera, web camera etc. have been assumed to be available before face matching. This leads to a major drawback in achieving fully automated heterogeneous face recognition as real world scenarios cannot be reflected. Therefore, modality identification is an important component of the heterogeneous face recognition system which is being overlooked by majority of the state-of-the-art methods. This component should be given similar attention when comparing with other face recognition modules identifying pose, gesture, camera source etc. In this paper inspired from sensor pattern noise (SPN) estimation based approaches, a novel image sharpening based modality pattern noise technique is proposed for modality identification. The proposed system has been evaluated on three challenging benchmarks of heterogeneous face databases. The proposed technique has produced outstanding results and will open new avenues of research for automated HFR methods in future

A machine learning-based approach for picture acquisition timeslot prediction using defective pixels

Estimating the acquisition time of digital photographs is a challenging task in temporal image forensics, but the application is highly demanded for establishing temporal order among individual pieces of evidence and deduce the causal relationship of events in a court case. The forensic investigator needs to identify the timeline of events and look for some patterns to gain a clear overview of activities associated with a crime. This paper aims to explore the presence of defective pixels over time for estimating the acquisition date of digital pictures. We propose a technique to predict the acquisition timeslots of digital pictures using a set of candidate defective pixels in non-overlapping image blocks. First, potential candidate defective pixels are determined through related pixel neighbourhood and two proposed features, called the local variation features to best fit in a machine learning model. The machine learning approach is used to model the temporal behaviour of camera sensor defects in each block using the scores obtained from individually trained pixel defect locations and fused in a majority voting method. Interestingly, timeslot estimation using individual blocks has been shown to be more accurate when virtual sub-classes corresponding to halved timeslots are first considered prior to the reconstruction step. Finally, the last stage of the system consists of the combination of block scores in a second majority voting operation to further enhance performance. Assessed on the NTIF image dataset, the proposed system has been shown to reach very promising results with an estimated accuracy between 88% and 93% and clear superiority over a related state-of-the-art system