This guide is intended to collect and give a brief summary of published papers that tried to solve Camera Model Identification.
\n\nDigital forensics got high attention in the past decade. Digital images could be edited, changed or falsified because of image editing software available today. Reliable forensic methods are needed by law enforcement agencies to restore the trust to digital images. In general, digital image forensics involves two key problems: image origin identification and image forgery detection. The problem of image origin identification goal is to predict if an image is acquired by a specific camera or determine camera model. Image forgery detection aim is to detect any manipulation. This competition targets the first problem type which is identification of image origin.
\n\nDigital cameras consist of lens system, filters, color filter array (CFA), image sensor, and digital image processor (DIP). Color images may suffer from aberrations caused by the lenses, such as chromatic aberration and spherical aberration. Chromatic aberration is the failure to converge different wavelengths at the same position on the sensor, while spherical aberration causes light passing through the periphery of the spherical lens to converge at a point closer to the lens than light passing through the lens center.
\n\nAfter passing through the lenses, light goes through a set of filters. An infrared filter is an absorptive or reflective filter allowing only the visible part of the spectrum to pass, while blocking infrared radiation that can decrease the sharpness of the formed image. An anti-aliasing filter reduces aliasing, a phenomenon that happens when the spacing between pixels in the sensor cannot support the finer spatial frequency of the target objects such as decorative patterns.
\n\nAt the heart of a digital camera is the image sensor. An image sensor is an array of rows and columns of photodiode elements, or pixels. When light strikes the pixel array, each pixel generates an analog signal proportional to the intensity of light, which is then converted to digital signal and processed by the DIP. Sensor pixels are not sensitive to colors; they just record the brightness of light, thus producing a monochromatic output.
\n\nTo produce a color image, a color filter array (CFA) is used in front of the sensor so that each pixel records the light intensity for a single color only. The output from the sensor with a Bayer filter is a mosaic of red, green and blue pixels of different intensities. Since each pixel record only one of the three colors, the full color image is accomplished by the DIP using various interpolation (demosaicking) algorithms reduction, matrix manipulation, image sharpening, aperture correction, and gamma correction to produce a good quality image.
\n\nThis section aims to summarize different approaches used to solve camera model identification challenge.
\n\nPropose the lens radial distortion as a fingerprint to identify source camera. Radial distortion causes straight lines to appear as curved lines on the output images and it occurs when the transverse magnification MT (ratio of the image distance to the object distance) is not a constant but a function of the off-axis image distance r.
\n\nPaper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.7686&rep=rep1&type=pdf
\n\nExamine the defects of CCD pixels and use them to match target images to source digital camera. Pixel defects include point defects, hot point defects, dead pixel, pixel traps, and cluster defects.
\n\nPapers:
\n\nA reliable method for identifying source camera based on sensor pattern noise. The pixel non-uniformity (PNU), where different pixels have different light sensitivities due to imperfections in sensor manufacturing processes, is a major source of pattern noise. This makes PNU a natural feature for uniquely identifying sensors.
\n\nPaper: http://ieeexplore.ieee.org/document/1530330/
\n\nExplore the CFA interpolation process to determine the correlation structure present in each color band which can be used for image classification. The main assumption is that the interpolation algorithm and the design of the CFA filter pattern of each manufacturer (or even each camera model) are somewhat different from others, which will result in distinguishable correlation structures in the captured images. \nPaper: http://ieeexplore.ieee.org/document/4064593/
\n\nObtain a coefficient matrix from a quadratic pixel correlation model where spatially periodic inter-pixel correlation follows a quadratic form.
\n\nIdentify a set of image features that can be used to uniquely classify a camera model.
\n\nThe most famous and specifically designed dataset for the camera model identification problem is the Dresden Image database. The website of the database: http://forensics.inf.tu-dresden.de/ddimgdb/.
", "messages": [ { "id": 268120, "postDate": "2018-01-13T12:27:22.970Z", "content": "This guide is intended to collect and give a brief summary of published papers that tried to solve Camera Model Identification.
\n\nDigital forensics got high attention in the past decade. Digital images could be edited, changed or falsified because of image editing software available today. Reliable forensic methods are needed by law enforcement agencies to restore the trust to digital images. In general, digital image forensics involves two key problems: image origin identification and image forgery detection. The problem of image origin identification goal is to predict if an image is acquired by a specific camera or determine camera model. Image forgery detection aim is to detect any manipulation. This competition targets the first problem type which is identification of image origin.
\n\nDigital cameras consist of lens system, filters, color filter array (CFA), image sensor, and digital image processor (DIP). Color images may suffer from aberrations caused by the lenses, such as chromatic aberration and spherical aberration. Chromatic aberration is the failure to converge different wavelengths at the same position on the sensor, while spherical aberration causes light passing through the periphery of the spherical lens to converge at a point closer to the lens than light passing through the lens center.
\n\nAfter passing through the lenses, light goes through a set of filters. An infrared filter is an absorptive or reflective filter allowing only the visible part of the spectrum to pass, while blocking infrared radiation that can decrease the sharpness of the formed image. An anti-aliasing filter reduces aliasing, a phenomenon that happens when the spacing between pixels in the sensor cannot support the finer spatial frequency of the target objects such as decorative patterns.
\n\nAt the heart of a digital camera is the image sensor. An image sensor is an array of rows and columns of photodiode elements, or pixels. When light strikes the pixel array, each pixel generates an analog signal proportional to the intensity of light, which is then converted to digital signal and processed by the DIP. Sensor pixels are not sensitive to colors; they just record the brightness of light, thus producing a monochromatic output.
\n\nTo produce a color image, a color filter array (CFA) is used in front of the sensor so that each pixel records the light intensity for a single color only. The output from the sensor with a Bayer filter is a mosaic of red, green and blue pixels of different intensities. Since each pixel record only one of the three colors, the full color image is accomplished by the DIP using various interpolation (demosaicking) algorithms reduction, matrix manipulation, image sharpening, aperture correction, and gamma correction to produce a good quality image.
\n\nThis section aims to summarize different approaches used to solve camera model identification challenge.
\n\nPropose the lens radial distortion as a fingerprint to identify source camera. Radial distortion causes straight lines to appear as curved lines on the output images and it occurs when the transverse magnification MT (ratio of the image distance to the object distance) is not a constant but a function of the off-axis image distance r.
\n\nPaper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.7686&rep=rep1&type=pdf
\n\nExamine the defects of CCD pixels and use them to match target images to source digital camera. Pixel defects include point defects, hot point defects, dead pixel, pixel traps, and cluster defects.
\n\nPapers:
\n\nA reliable method for identifying source camera based on sensor pattern noise. The pixel non-uniformity (PNU), where different pixels have different light sensitivities due to imperfections in sensor manufacturing processes, is a major source of pattern noise. This makes PNU a natural feature for uniquely identifying sensors.
\n\nPaper: http://ieeexplore.ieee.org/document/1530330/
\n\nExplore the CFA interpolation process to determine the correlation structure present in each color band which can be used for image classification. The main assumption is that the interpolation algorithm and the design of the CFA filter pattern of each manufacturer (or even each camera model) are somewhat different from others, which will result in distinguishable correlation structures in the captured images. \nPaper: http://ieeexplore.ieee.org/document/4064593/
\n\nObtain a coefficient matrix from a quadratic pixel correlation model where spatially periodic inter-pixel correlation follows a quadratic form.
\n\nIdentify a set of image features that can be used to uniquely classify a camera model.
\n\nThe most famous and specifically designed dataset for the camera model identification problem is the Dresden Image database. The website of the database: http://forensics.inf.tu-dresden.de/ddimgdb/.
", "rawMarkdown": "# Introduction\n\nThis guide is intended to collect and give a brief summary of published papers that tried to solve Camera Model Identification. \n\nDigital forensics got high attention in the past decade. Digital images could be edited, changed or falsified because of image editing software available today. Reliable forensic methods are needed by law enforcement agencies to restore the trust to digital images. In general, digital image forensics involves two key problems: image origin identification and image forgery detection. The problem of image origin identification goal is to predict if an image is acquired by a specific camera or determine camera model. Image forgery detection aim is to detect any manipulation. This competition targets the first problem type which is identification of image origin. \n\nDigital cameras consist of lens system, filters, color filter array (CFA), image sensor, and digital image processor (DIP). Color images may suffer from aberrations caused by the lenses, such as chromatic aberration and spherical aberration. Chromatic aberration is the failure to converge different wavelengths at the same position on the sensor, while spherical aberration causes light passing through the periphery of the spherical lens to converge at a point closer to the lens than light passing through the lens center. \n\nAfter passing through the lenses, light goes through a set of filters. An infrared filter is an absorptive or reflective filter allowing only the visible part of the spectrum to pass, while blocking infrared radiation that can decrease the sharpness of the formed image. An anti-aliasing filter reduces aliasing, a phenomenon that happens when the spacing between pixels in the sensor cannot support the finer spatial frequency of the target objects such as decorative patterns. \n\nAt the heart of a digital camera is the image sensor. An image sensor is an array of rows and columns of photodiode elements, or pixels. When light strikes the pixel array, each pixel generates an analog signal proportional to the intensity of light, which is then converted to digital signal and processed by the DIP. Sensor pixels are not sensitive to colors; they just record the brightness of light, thus producing a monochromatic output. \n\nTo produce a color image, a color filter array (CFA) is used in front of the sensor so that each pixel records the light intensity for a single color only. The output from the sensor with a Bayer filter is a mosaic of red, green and blue pixels of different intensities. Since each pixel record only one of the three colors, the full color image is accomplished by the DIP using various interpolation (demosaicking) algorithms reduction, matrix manipulation, image sharpening, aperture correction, and gamma correction to produce a good quality image.\n \n# Algorithms \nThis section aims to summarize different approaches used to solve camera model identification challenge. \n## Using Lens Aberration\nPaper: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6069/1/Source-camera-identification-using-footprints-from-lens-aberration/10.1117/12.649775.short?SSO=1 \n\nPropose the lens radial distortion as a fingerprint to identify source camera. Radial distortion causes straight lines to appear as curved lines on the output images and it occurs when the transverse magnification MT (ratio of the image distance to the object distance) is not a constant but a function of the off-axis image distance r.\n## Using Sensor Imperfection\nPaper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.7686&rep=rep1&type=pdf \n\nExamine the defects of CCD pixels and use them to match target images to source digital camera. Pixel defects include point defects, hot point defects, dead pixel, pixel traps, and cluster defects. \n\nPapers: \n\n - http://ieeexplore.ieee.org/document/1634362/ \n - https://www.sciencedirect.com/science/article/pii/S1051200415003012 \n - http://ieeexplore.ieee.org/document/6662407/ \n - http://ieeexplore.ieee.org/document/4284577/\n\nA reliable method for identifying source camera based on sensor pattern noise. The pixel non-uniformity (PNU), where different pixels have different light sensitivities due to imperfections in sensor manufacturing processes, is a major source of pattern noise. This makes PNU a natural feature for uniquely identifying sensors. \n## Using CFA Interpolation\nPaper: http://ieeexplore.ieee.org/document/1530330/ \n\nExplore the CFA interpolation process to determine the correlation structure present in each color band which can be used for image classification. The main assumption is that the interpolation algorithm and the design of the CFA filter pattern of each manufacturer (or even each camera model) are somewhat different from others, which will result in distinguishable correlation structures in the captured images. \nPaper: http://ieeexplore.ieee.org/document/4064593/ \n\nObtain a coefficient matrix from a quadratic pixel correlation model where spatially periodic inter-pixel correlation follows a quadratic form.\n## Using Image Features\n- http://ieeexplore.ieee.org/document/\n- http://ieeexplore.ieee.org/document/1660338/ \n\nIdentify a set of image features that can be used to uniquely classify a camera model. \n# Datasets \nThe most famous and specifically designed dataset for the camera model identification problem is the Dresden Image database. The website of the database: http://forensics.inf.tu-dresden.de/ddimgdb/. \n\n\n [1]: https://ibb.co/jRyNCR", "votes": 13 } ], "comments": [], "raw_markdown_by_id": { "268120": "# Introduction\n\nThis guide is intended to collect and give a brief summary of published papers that tried to solve Camera Model Identification. \n\nDigital forensics got high attention in the past decade. Digital images could be edited, changed or falsified because of image editing software available today. Reliable forensic methods are needed by law enforcement agencies to restore the trust to digital images. In general, digital image forensics involves two key problems: image origin identification and image forgery detection. The problem of image origin identification goal is to predict if an image is acquired by a specific camera or determine camera model. Image forgery detection aim is to detect any manipulation. This competition targets the first problem type which is identification of image origin. \n\nDigital cameras consist of lens system, filters, color filter array (CFA), image sensor, and digital image processor (DIP). Color images may suffer from aberrations caused by the lenses, such as chromatic aberration and spherical aberration. Chromatic aberration is the failure to converge different wavelengths at the same position on the sensor, while spherical aberration causes light passing through the periphery of the spherical lens to converge at a point closer to the lens than light passing through the lens center. \n\nAfter passing through the lenses, light goes through a set of filters. An infrared filter is an absorptive or reflective filter allowing only the visible part of the spectrum to pass, while blocking infrared radiation that can decrease the sharpness of the formed image. An anti-aliasing filter reduces aliasing, a phenomenon that happens when the spacing between pixels in the sensor cannot support the finer spatial frequency of the target objects such as decorative patterns. \n\nAt the heart of a digital camera is the image sensor. An image sensor is an array of rows and columns of photodiode elements, or pixels. When light strikes the pixel array, each pixel generates an analog signal proportional to the intensity of light, which is then converted to digital signal and processed by the DIP. Sensor pixels are not sensitive to colors; they just record the brightness of light, thus producing a monochromatic output. \n\nTo produce a color image, a color filter array (CFA) is used in front of the sensor so that each pixel records the light intensity for a single color only. The output from the sensor with a Bayer filter is a mosaic of red, green and blue pixels of different intensities. Since each pixel record only one of the three colors, the full color image is accomplished by the DIP using various interpolation (demosaicking) algorithms reduction, matrix manipulation, image sharpening, aperture correction, and gamma correction to produce a good quality image.\n \n# Algorithms \nThis section aims to summarize different approaches used to solve camera model identification challenge. \n## Using Lens Aberration\nPaper: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6069/1/Source-camera-identification-using-footprints-from-lens-aberration/10.1117/12.649775.short?SSO=1 \n\nPropose the lens radial distortion as a fingerprint to identify source camera. Radial distortion causes straight lines to appear as curved lines on the output images and it occurs when the transverse magnification MT (ratio of the image distance to the object distance) is not a constant but a function of the off-axis image distance r.\n## Using Sensor Imperfection\nPaper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.7686&rep=rep1&type=pdf \n\nExamine the defects of CCD pixels and use them to match target images to source digital camera. Pixel defects include point defects, hot point defects, dead pixel, pixel traps, and cluster defects. \n\nPapers: \n\n - http://ieeexplore.ieee.org/document/1634362/ \n - https://www.sciencedirect.com/science/article/pii/S1051200415003012 \n - http://ieeexplore.ieee.org/document/6662407/ \n - http://ieeexplore.ieee.org/document/4284577/\n\nA reliable method for identifying source camera based on sensor pattern noise. The pixel non-uniformity (PNU), where different pixels have different light sensitivities due to imperfections in sensor manufacturing processes, is a major source of pattern noise. This makes PNU a natural feature for uniquely identifying sensors. \n## Using CFA Interpolation\nPaper: http://ieeexplore.ieee.org/document/1530330/ \n\nExplore the CFA interpolation process to determine the correlation structure present in each color band which can be used for image classification. The main assumption is that the interpolation algorithm and the design of the CFA filter pattern of each manufacturer (or even each camera model) are somewhat different from others, which will result in distinguishable correlation structures in the captured images. \nPaper: http://ieeexplore.ieee.org/document/4064593/ \n\nObtain a coefficient matrix from a quadratic pixel correlation model where spatially periodic inter-pixel correlation follows a quadratic form.\n## Using Image Features\n- http://ieeexplore.ieee.org/document/\n- http://ieeexplore.ieee.org/document/1660338/ \n\nIdentify a set of image features that can be used to uniquely classify a camera model. \n# Datasets \nThe most famous and specifically designed dataset for the camera model identification problem is the Dresden Image database. The website of the database: http://forensics.inf.tu-dresden.de/ddimgdb/. \n\n\n [1]: https://ibb.co/jRyNCR" } }