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

Image source camera attribution

Orientador: Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Verificar a integridade e a autenticidade de imagens digitais é de fundamental importância quando estas podem ser apresentadas como evidência em uma corte de justiça. Uma maneira de se realizar esta verificação é identificar a câmera digital que capturou tais imagens. Neste trabalho, nós discutimos abordagens que permitem identificar se uma imagem sob investigação foi ou não capturada por uma determinada câmera digital. A pesquisa foi realizada segundo duas óticas: (1) verificação, em que o objetivo é verificar se uma determinada câmera, de fato, capturou uma dada imagem; e (2) reconhecimento, em que o foco é verificar se uma determinada imagem foi obtida por alguma câmera (se alguma) dentro de um conjunto limitado de câmeras e identificar, em caso afirmativo, o dispositivo específico que efetuou a captura. O estudo destas abordagens foi realizado considerando um cenário aberto (open-set), no qual nem sempre temos acesso a alguns dos dispositivos em questão. Neste trabalho, tratamos, também, do problema de correspondência entre dispositivos, em que o objetivo é verificar se um par de imagens foi gerado por uma mesma câmera. Isto pode ser útil para agrupar conjuntos de imagens de acordo com sua fonte quando não se possui qualquer informação sobre possíveis dispositivos de origem. As abordagens propostas apresentaram bons resultados, mostrando-se capazes de identificar o dispositivo específico utilizado na captura de uma imagem, e não somente sua marcaAbstract: Image's integrity and authenticity verification is paramount when it comes to a court of law. Just like we do in ballistics tests when we match a gun to its bullets, we can identify a given digital camera that acquired an image under investigation. In this work, we discussed approaches for identifying whether or not a given image under investigation was captured by a specific digital camera. We carried out the research under two vantage points: (1) verification, in which we are interested in verifying whether or not a given camera captured an image under investigation; and (2) recognition, in which we want to verify if an image was captured by a given camera (if any) from a pool of devices, and to point out such a camera. We performed this investigation considering an open set scenario, under which we can not rely on the assumption of full access to all of the investigated devices. We also tried to solve the device linking problem, where we aim at verifying if an image pair was generated by the same camera, without any information about the source of images. Our approaches reported good results, in terms of being capable of identifying the specific device that captured a given image including its model, brand, and even serial numberMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Towards a Unified Theory of Sensor Pattern Noise: An analysis of dark current, lens effects, and temperature bias in CMOS image sensors

Matching images to a discrete camera is of significance in forensic investigation. In the case of digital images, forensic matching is possible through the use of sensor noise present within every image. There exist misconceptions, however, around how this noise reacts under variables such as temperature and the use of different lens systems. This study aims to formulate a revised model of the additive noise for an image sensor to determine if a new method for matching images to sensors could be created which uses fewer resources than the existing methods, and takes into account a wider range of environmental conditions. Specifically, a revised noise model was needed to determine the effects of different lens systems and the impact of temperature on sensor noise. To determine the revised model, an updated literature search was conducted on the background theory relating to CMOS sensors, as the existing work focuses on CCD imaging sensors. This theory was then applied using six off the shelf CMOS imaging sensors with integrated lens systems. An image sensor was examined under scanning electron microscopy and through the use of Energydispersive x-ray spectroscopy the non-uniform structure of individual pixels was visually observed within the sensor. The lens systems were removed and made interchangeable through the use of a 3D printed camera housing. Lens effects were assessed by swapping lens systems between the cameras and using a pinhole lens to remove all optical effects. The temperature was controlled using an Arduino controlled Peltier plate device, and dark current images were obtained at different temperatures using a blackout lens. It was observed that dark current could be used to identify the temperature of the image sensor at the time of acquisition, contrary to the statements in existing literature that sensor pattern noise is temperature invariant. It was shown that the lens system contributes approximately a quarter of the signal power xii used for pattern matching between the image and sensor. Moreover, through the use of targeted signal processing methods and simple ”Goldilocks” filters processing times could be reduced by more than half by sacrificing precision without losing accuracy. This work indicates that sensor pattern noise, while already viable for forensic identification of images to a specific camera, can also be used for identification of an image to a specific lens system and an image sensors temperature. It has also shown that a tool using sensor pattern noise may have a viable future as a forensic method of triage when confronted with large image data sets. Such additional information could prove effective for forensic investigators, intelligence agencies and police when faced with any form of crime involving imaging technology such as fraud, child exploitation or terrorism.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 201

Scanner Identification Using Sensor Pattern Noise

Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In many cases it is important to be able to determine the source of a digital image. This paper presents methods for authenticating images that have been acquired using flatbed desktop scanners. The method is based on using the pattern noise of the imaging sensor as a fingerprint for the scanner, similar to methods that have been reported for identifying digital cameras. To identify the source scanner of an image a reference pattern is estimated for each scanner and is treated as a unique fingerprint of the scanner. An anisotropic local polynomial estimator is used for obtaining the reference patterns. To further improve the classification accuracy a feature vector based approach using an SVM classifier is used to classify the pattern noise. This feature vector based approach is shown to achieve a high classification accuracy