21-05-2012, 04:47 PM
Automatic source camera identificationusing the
intrinsic lens radial distortion
[attachment=22620]
Abstract:
Source camera identification refers to the task of matching digital images with the cameras that are responsible for producing these
images. This is an important task in image forensics, which in turn is a
critical procedure in law enforcement. Unfortunately, few digital cameras
are equipped with the capability of producing watermarks for this purpose. In this paper, we demonstrate that it is possible to achieve a high rate of accuracy in the identification by noting the intrinsic lens radial distortion of each camera. To reduce manufacturing cost, the majority of digital cameras are equipped with lenses having rather spherical surfaces, whose inherent radial distortions serve as unique fingerprints in the images. We extract, for each image, parameters from aberration measurements, which are then used to train and test a support vector machine classifier. We conduct extensive experiments to evaluate the success rate of a source camera identification with five cameras. The results show that this is a viable approach with high
accuracy. Additionally, we also present results on how the error rates may change with images captured using various optical zoom levels, as zooming is commonly available in digital cameras.
Introduction
The purpose of this research is to demonstrate that it is possible to exploit lens aberrations for source camera identification. It is generally accepted that most consumer-level cameras optics deviates from ideal pinhole camera model [1, 2, 3]. Among different kinds of aberrations, lens radial distortion is the most severe. The inherent lens radial distortion causes non-linear geometrical distortion on the images. In this paper, we propose to estimate the lens radial distortion from an image and use it to identify the source camera of the image. Source camera identification is useful in image forensics. With the availability of powerful software, digital images can be manipulated easily even by amateurs and the alterations may leave no observable traces. This hinders the credibility of digital images presented as news items or as evidence in court cases. As a result, in image forensics, one would like to ascertain the authenticity of a digital image by identifying the source camera of an image. In this paper, we focus on distinguishing between images captured by a limited number of camera models. The problem of source camera identification can be approached from several directions. An obvious approach is to examine an image file’s header. For example, Exchangeable Image File Format (EXIF) header is attached in JPEG images by most consumer-level cameras. Information such as digital camera type, exposure, date and time of an image is included in the header. One can determine the source camera of an image from this information. However, this information is fragile. It may be maliciously altered or discarded after image editing.
Lens radial distortion
2.1. The imaging system
In a digital camera, the light from the scene passes through the camera’s lens system, an antialiasing filter, and color filter array, and finally reaches the camera’s sensor [4, 5]. Each light sensing element of the sensor array integrates the incident light over the whole spectrum and obtains an electric signal representation of the scenery. The electric signal is digitalized by an analog-to-digital converter. Then the digital signal is processed by color processing algorithms built in the camera chips. These color processing algorithms includes demosaicing, color correction, white balancing, and gamma correction [13].
Discussions and future work
In Section 4.2 fig. 5, we have shown a scatter plot of radial distortion parameters, k 1 and k2. The outliers in the plot are images with very short lines and inadequate number of long straight lines. Short straight lines usually contain more noise than useful information about distortion. An example Casio image with short segments and its edge map is shown in fig. 8 (top). The estimated k1 and k2 is 0.049 and -0.014 respectively. The lack of long edges in the example image
leads to wrong estimation of the lens distortion. Apart from the length of the straight lines, the position of the line also affects the lens distortion estimation. Since the radial distortion is a function of the radius from the center of distortion, the further away a line from the center, the more sever the distortion. If a line is close to the center, it will be less severely distorted and will provide less useful information for distortion estimation. An example image from Casio of this kind is shown in fig. 8 (bottom).We only used the straight line near the center for distortion estimation and the estimated k1 and k2 is 0.037 and 0.023 respectively. The k2 and k2 values from both examples are far from the majority of Casio images.
Conclusion
In this paper, we examine the use of lens footprints left on the images in identifying the source camera of a digital image. We propose to use the lens radial distortion on the images for this problem. A classifier based on lens radial distortion is built and used to evaluate the effectiveness of this feature.We show that it is feasible to use the lens radial distortion to classify images originating from a five-camera model.We also propose to incorporate our lens radial distortion with the statistics obtained from image intensities for image classification. We demonstrate that comparing with the procedures using only statistics from image intensities, our approach shows a statistical improvement in accuracy. Since the lens distortion parameters vary with focal length, we also investigate the effectiveness of our lens distortion parameters, k 1 and k2, in an image dataset with various optical zoom.
intrinsic lens radial distortion
[attachment=22620]
Abstract:
Source camera identification refers to the task of matching digital images with the cameras that are responsible for producing these
images. This is an important task in image forensics, which in turn is a
critical procedure in law enforcement. Unfortunately, few digital cameras
are equipped with the capability of producing watermarks for this purpose. In this paper, we demonstrate that it is possible to achieve a high rate of accuracy in the identification by noting the intrinsic lens radial distortion of each camera. To reduce manufacturing cost, the majority of digital cameras are equipped with lenses having rather spherical surfaces, whose inherent radial distortions serve as unique fingerprints in the images. We extract, for each image, parameters from aberration measurements, which are then used to train and test a support vector machine classifier. We conduct extensive experiments to evaluate the success rate of a source camera identification with five cameras. The results show that this is a viable approach with high
accuracy. Additionally, we also present results on how the error rates may change with images captured using various optical zoom levels, as zooming is commonly available in digital cameras.
Introduction
The purpose of this research is to demonstrate that it is possible to exploit lens aberrations for source camera identification. It is generally accepted that most consumer-level cameras optics deviates from ideal pinhole camera model [1, 2, 3]. Among different kinds of aberrations, lens radial distortion is the most severe. The inherent lens radial distortion causes non-linear geometrical distortion on the images. In this paper, we propose to estimate the lens radial distortion from an image and use it to identify the source camera of the image. Source camera identification is useful in image forensics. With the availability of powerful software, digital images can be manipulated easily even by amateurs and the alterations may leave no observable traces. This hinders the credibility of digital images presented as news items or as evidence in court cases. As a result, in image forensics, one would like to ascertain the authenticity of a digital image by identifying the source camera of an image. In this paper, we focus on distinguishing between images captured by a limited number of camera models. The problem of source camera identification can be approached from several directions. An obvious approach is to examine an image file’s header. For example, Exchangeable Image File Format (EXIF) header is attached in JPEG images by most consumer-level cameras. Information such as digital camera type, exposure, date and time of an image is included in the header. One can determine the source camera of an image from this information. However, this information is fragile. It may be maliciously altered or discarded after image editing.
Lens radial distortion
2.1. The imaging system
In a digital camera, the light from the scene passes through the camera’s lens system, an antialiasing filter, and color filter array, and finally reaches the camera’s sensor [4, 5]. Each light sensing element of the sensor array integrates the incident light over the whole spectrum and obtains an electric signal representation of the scenery. The electric signal is digitalized by an analog-to-digital converter. Then the digital signal is processed by color processing algorithms built in the camera chips. These color processing algorithms includes demosaicing, color correction, white balancing, and gamma correction [13].
Discussions and future work
In Section 4.2 fig. 5, we have shown a scatter plot of radial distortion parameters, k 1 and k2. The outliers in the plot are images with very short lines and inadequate number of long straight lines. Short straight lines usually contain more noise than useful information about distortion. An example Casio image with short segments and its edge map is shown in fig. 8 (top). The estimated k1 and k2 is 0.049 and -0.014 respectively. The lack of long edges in the example image
leads to wrong estimation of the lens distortion. Apart from the length of the straight lines, the position of the line also affects the lens distortion estimation. Since the radial distortion is a function of the radius from the center of distortion, the further away a line from the center, the more sever the distortion. If a line is close to the center, it will be less severely distorted and will provide less useful information for distortion estimation. An example image from Casio of this kind is shown in fig. 8 (bottom).We only used the straight line near the center for distortion estimation and the estimated k1 and k2 is 0.037 and 0.023 respectively. The k2 and k2 values from both examples are far from the majority of Casio images.
Conclusion
In this paper, we examine the use of lens footprints left on the images in identifying the source camera of a digital image. We propose to use the lens radial distortion on the images for this problem. A classifier based on lens radial distortion is built and used to evaluate the effectiveness of this feature.We show that it is feasible to use the lens radial distortion to classify images originating from a five-camera model.We also propose to incorporate our lens radial distortion with the statistics obtained from image intensities for image classification. We demonstrate that comparing with the procedures using only statistics from image intensities, our approach shows a statistical improvement in accuracy. Since the lens distortion parameters vary with focal length, we also investigate the effectiveness of our lens distortion parameters, k 1 and k2, in an image dataset with various optical zoom.