On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, Photo Response Non_Uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the 'Dresden image databas

Coherence of PRNU weighted estimations for improved source camera identification

This paper presents a method for Photo Response Non Uniformity (PRNU) pattern noise based camera identification. It takes advantage of the coherence between different PRNU estimations restricted to specific image regions. The main idea is based on the following observations: different methods can be used for estimating PRNU contribution in a given image; the estimation has not the same accuracy in the whole image as a more faithful estimation is expected from flat regions. Hence, two different estimations of the reference PRNU have been considered in the classification procedure, and the coherence of the similarity metric between them, when evaluated in three different image regions, is used as classification feature. More coherence is expected in case of matching, i.e. the image has been acquired by the analysed device, than in the opposite case, where similarity metric is almost noisy and then unpredictable. Presented results show that the proposed approach provides comparable and often better classification results of some state of the art methods, showing to be robust to lack of flat field (FF) images availability, devices of the same brand or model, uploading/downloading from social networks

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Digital Camera Identification Using Neural Network Algorithm And Pattern Noise In Imaging Sensors

In forensic investigation of criminal cases like child pornography, image forgery, identity theft, steganography, movie piracy, insurance claims, and other cases of scientific frauds, some of the most significant challenges may be to detect the origin of an image or the photographing camera, detect forged images or hidden messages in images from retrieved digital evidence. There has been much interest in developing camera fingerprints for the forensic task of digital camera identification; that is, to be able to tie an image to it\u27s photographing camera with high certainty or good assurance metrics, specially when the camera is not present in the crime scene. Inspired by the existing approaches of camera fingerprint forensics, this paper explores a novel approach for camera identification, based on PRNU noise fingerprint, using Artificial Neural Network (ANN) algorithm. While statistical algorithms produce probabilistic inferences based on statistical problem data, artificial neural network algorithm learns features about the problem from training data. Based on correctness of feature representations and complex mathematical processing on the training data, the neural network is able to learn or approximate any non-linear distribution very easily. As it trains on different examples, it\u27s generalization performance on new inputs improves. In currently proposed work, first the reference fingerprint and test fingerprint are estimated based on a simple kernel based processing algorithm for PRNU coefficient estimation. Then an artificial neural network is set up in C programming language for PRNU pattern recognition based on the estimated feature values from the reference pattern data. The network is presented with training inputs and desired outputs, and based on formulated assumptions and hypothesis described in later sections, the expectation is that the ANN will be able to recognize PRNU fingerprint in images taken by the same camera whose fingerprint the ANN got trained on. A low Mean Square Error (MSE) during ANN training and testing is an indication that the ANN could report with high confidence, a match between the camera fingerprint pattern and the pattern in test image. Multilayer Perceptron (MLP) ANN with single hidden layer is proved to be a universal non-linear function approximator and can be applied to solve any complex non-linear problem. Current approach uses back propagation MLP ANN algorithm for fingerprint detection or camera identification

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Photo response non-uniformity based image forensics in the presence of challenging factors

With the ever-increasing prevalence of digital imaging devices and the rapid development of networks, the sharing of digital images becomes ubiquitous in our daily life. However, the pervasiveness of powerful image-editing tools also makes the digital images an easy target for malicious manipulations. Thus, to prevent people from falling victims to fake information and trace the criminal activities, digital image forensics methods like source camera identification, source oriented image clustering and image forgery detections have been developed. Photo response non-uniformity (PRNU), which is an intrinsic sensor noise arises due to the pixels non-uniform response to the incident, has been used as a powerful tool for image device fingerprinting. The forensic community has developed a vast number of PRNU-based methods in different fields of digital image forensics. However, with the technology advancement in digital photography, the emergence of photo-sharing social networking sites, as well as the anti-forensics attacks targeting the PRNU, it brings new challenges to PRNU-based image forensics. For example, the performance of the existing forensic methods may deteriorate due to different camera exposure parameter settings and the efficacy of the PRNU-based methods can be directly challenged by image editing tools from social network sites or anti-forensics attacks. The objective of this thesis is to investigate and design effective methods to mitigate some of these challenges on PRNU-based image forensics. We found that the camera exposure parameter settings, especially the camera sensitivity, which is commonly known by the name of the ISO speed, can influence the PRNU-based image forgery detection. Hence, we first construct the Warwick Image Forensics Dataset, which contains images taken with diverse exposure parameter settings to facilitate further studies. To address the impact from ISO speed on PRNU-based image forgery detection, an ISO speed-specific correlation prediction process is proposed with a content-based ISO speed inference method to facilitate the process even if the ISO speed information is not available. We also propose a three-step framework to allow the PRNUbased source oriented clustering methods to perform successfully on Instagram images, despite some built-in image filters from Instagram may significantly distort PRNU. Additionally, for the binary classification of detecting whether an image's PRNU is attacked or not, we propose a generative adversarial network-based training strategy for a neural network-based classifier, which makes the classifier generalize better for images subject to unprecedented attacks. The proposed methods are evaluated on public benchmarking datasets and our Warwick Image Forensics Dataset, which is released to the public as well. The experimental results validate the effectiveness of the methods proposed in this thesis

Star Imager For Nanosatellite Applications

This research examines the feasibility of Commercial-off-the-shelf Complementary Metal-Oxide-Semiconductor image sensors for use on nanosatellites as a star imager. An emphasis is placed on method selection and implementation of the star imager algorithm: Centroiding, Identification and Attitude Determination. The star imager algorithm makes use of the Lost-in-Space condition to provide attitude knowledge for each image. Flat Field, Checker Board and Point Spread Function calibration methods were employed to characterize the star imager. Finally, feasibility testing of the star imager is accomplished through simulations and night sky images

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis