An Analysis of Optical Contributions to a Photo-Sensor's Ballistic Fingerprints

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.Comment: 16 pages, 9 figures, preprint for journal submission, paper is based on a thesis chapte

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

Review on passive approaches for detecting image tampering

This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below

Digital Video Inpainting Detection Using Correlation Of Hessian Matrix

The use of digital video during forensic investigation helps in providing evidence related to crime scene. However, due to freely available user friendly video editing tools, the forgery of acquired digital videos that are used as evidence in a law suit is now simpler and faster. As a result, it has become easier for manipulators to alter the contents of digital evidence. For instance, inpainting technique is used to remove an object from a video without leaving any artefact of illegal tampering. Therefore, this paper presents a technique for detecting and locating inpainting forgery in a video sequence with static camera motion. Our technique exploits statistical correlation of Hessian matrix (SCHM) to detect and locate tampered regions within a video sequence. The results of our experiments prove that the technique effectively detect and locate areas which are tampered using both texture and structure based inpainting with an average precision rate of 99.79% and an average false positive rate of 0.29%

Autenticación de imágenes digitales mediante patrones locales de texturas

La autenticidad de una imagen digital sufre graves amenazas debido a la existencia de poderosas herramientas para la edición de imágenes digitales que facilitan la modificación del contenido de las mismas sin dejar huellas visibles de tales cambios. Este problema unido a la facilidad de distribución de la información a través de plataformas digitales como blogs, Internet o redes sociales, ha provocado que la sociedad tienda a aceptar como cierto todo lo que ve sin cuestionar su veracidad. En este trabajo se propone un método de autenticación de imágenes digitales mediante el análisis de patrones locales de textura. El sistema propuesto combina el patrón binario local con la transformada discreta wavelet y la transformada discreta del coseno para extraer las características de cada uno de los bloques de la imagen investigada. Posteriormente, se utiliza la máquina de soporte vectorial para crear el modelo que permita la verificación de la autenticidad de una imagen. Para la evaluación del método propuesto se realizaron experimentos con bases de datos públicas de imágenes falsificadas que son ampliamente utilizadas en la literatura

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

Identificación del modelo de cámara mediante Redes Neuronales Convolucionales

La identificación del modelo de cámara siempre ha sido uno de los campos principales del análisis forense de imágenes, ya que es la base para resolver una amplia gama de problemas forenses. Dado que el Deep Learning ha logrado un gran progreso en las tareas de visión por computador, ha surgido un gran interés en la aplicación del aprendizaje profundo en imágenes forenses. En este documento, se propone un método de identificación de modelo de cámara basado en redes neuronales convolucionales profundas (CNNs). A diferencia de los métodos tradicionales, las CNNs pueden extraer características de forma au-tomática y simultánea y aprender a clasificar durante el proceso de aprendizaje. En el presente trabajo se describe un enfoque de aprendizaje profundo para el problema de detección de cámara entre 3 modelos diferentes como parte del IEEE Signal Processing Cup 2018: Camera Model Identification organizado por IEEE Signal Processing Society. Los experimentos muestran que podemos detectar modelos de cámara desconocidos con una precisión de más del 90%.Source camera model identification has always been one of the main fields of digital image forensics since it is the foundation of solving a wide range of forensic problems. Several effective camera model identification algorithms have been developed for the practical necessity. However, they are mostly based on traditional machine learning methods. Since Deep Learning has made great progress in computer vision tasks, significant interest has arisen in applying Deep Learning in image forensics. In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike tradi-tional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. In the current work, we describe our Deep Learning approach to the camera detection task of 3 cameras as a part of the IEEE Signal Processing Cup 2018: Camera Model Identification hosted by IEEE Signal Processing Society. Experiments show that we can detect unknown camera models with an accuracy greater than 90%.Universidad de Sevilla. Máster en Ingeniería de Telecomunicació