Camera Fingerprint Extraction via Spatial Domain Averaged Frames

© 2005-2012 IEEE. Photo Response Non-Uniformity (PRNU) based camera attribution is an effective method to determine the source camera of a visual object (an image or a video). To apply this method, images or videos need to be obtained from a camera to create a 'camera fingerprint' which then can be compared against the PRNU of the query media whose origin is under question. The fingerprint extraction process can be time consuming when a large number of video frames or images have to be denoised. This may need to be done when the individual images have been subjected to high compression or other geometric processing such as video stabilization. This paper investigates a simple, yet effective and efficient technique to create a camera fingerprint when so many still images need to be denoised. The technique utilizes Spatial Domain Averaged (SDA) frames. An SDA-frame is the arithmetic mean of multiple still images. When it is used for fingerprint extraction, the number of denoising operations can be significantly decreased with little or no performance loss. Experimental results show that the proposed method can work more than 50 times faster than conventional methods while providing similar matching results

A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.Comment: 39 pages, 14 figure

Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics

Image and video forensics have recently gained increasing attention due to the proliferation of manipulated images and videos, especially on social media platforms, such as Twitter and Instagram, which spread disinformation and fake news. This survey explores image and video identification and forgery detection covering both manipulated digital media and generative media. However, media forgery detection techniques are susceptible to anti-forensics; on the other hand, such anti-forensics techniques can themselves be detected. We therefore further cover both anti-forensics and counter anti-forensics techniques in image and video. Finally, we conclude this survey by highlighting some open problems in this domain

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

Análise de propriedades intrínsecas e extrínsecas de amostras biométricas para detecção de ataques de apresentação

Orientadores: Anderson de Rezende Rocha, Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biométricos. No entanto, um desafio ainda em aberto é a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintéticas, a partir das informações biométricas originais de um usuário legítimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biométrica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintéticas. Por exemplo, em biometria facial, uma tentativa de ataque é caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vídeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em íris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de íris de um usuário-alvo ou mesmo padrões de textura sintéticas. Nos sistemas biométricos de impressão digital, os usuários impostores podem enganar o sensor biométrico usando réplicas dos padrões de impressão digital construídas com materiais sintéticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biométricos faciais, de íris e de impressão digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruído; em propriedades intrínsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de características. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponível contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuínos em um sistema biométrico facial, os quais foram coletados com a autorização do Comitê de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrínsecas das amostras biométricas relacionadas aos artefatos que são adicionados durante a fabricação das amostras sintéticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos métodos propostos na literature que se utilizam de métodos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrínsecas das faces, estimadas a partir da informação de sombras presentes em sua superfície; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de características relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos métodos considerados estado da arte para as diferentes modalidades biométricas consideradas nesta tese. A pesquisa também considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender características relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponíveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140069/2016-0 CNPq, 142110/2017-5CAPESCNP

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Bond-Selective Intensity Diffraction Tomography

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 seconds per volume, with a lateral and axial resolution of ~350 nm and ~1.1 micron, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 micron X 100 micron)