Deep Feature Learning for Intrinsic Signature Based Camera Discrimination
In this paper we consider the problem of "end-to-end" digital camera identification by considering sequence of images obtained from the cameras. The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digi...
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Tsinghua University Press
2022-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020006 |
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| author | Chaity Banerjee Tharun Kumar Doppalapudi Eduardo Pasiliao Tathagata Mukherjee |
| author_facet | Chaity Banerjee Tharun Kumar Doppalapudi Eduardo Pasiliao Tathagata Mukherjee |
| author_sort | Chaity Banerjee |
| collection | DOAJ |
| description | In this paper we consider the problem of "end-to-end" digital camera identification by considering sequence of images obtained from the cameras. The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal. However it is known that identifying a digital camera is possible by analyzing the camera's intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture. It is known that such methods are computationally intensive requiring expensive pre-processing steps. In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them. We conduct experiments using three custom datasets: the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features, the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting. Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework. These deep feature maps can in turn be used to disambiguate the cameras from each another. Our system is end-to-end, requires no complicated pre-processing steps and the trained model is computationally efficient during testing, paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments. Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution. |
| format | Article |
| id | doaj-art-c918a1ff745e401981771cb4042eb991 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Tsinghua University Press |
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| series | Big Data Mining and Analytics |
| spelling | doaj-art-c918a1ff745e401981771cb4042eb9912025-02-02T06:14:03ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-09-015320622710.26599/BDMA.2022.9020006Deep Feature Learning for Intrinsic Signature Based Camera DiscriminationChaity Banerjee0Tharun Kumar Doppalapudi1Eduardo Pasiliao2Tathagata Mukherjee3Department of Idustrial & Systems Engineering, University of Central Florida, Orlando, FL 32816, USADepartment of Computer Science, University of Alabama in Huntsville, Huntsville, AL 35806, USAAir Force Research Labs, United States Air Force, Eglin Air Force Base, Shalimar, FL 32579, USADepartment of Computer Science, University of Alabama in Huntsville, Huntsville, AL 35806, USAIn this paper we consider the problem of "end-to-end" digital camera identification by considering sequence of images obtained from the cameras. The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal. However it is known that identifying a digital camera is possible by analyzing the camera's intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture. It is known that such methods are computationally intensive requiring expensive pre-processing steps. In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them. We conduct experiments using three custom datasets: the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features, the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting. Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework. These deep feature maps can in turn be used to disambiguate the cameras from each another. Our system is end-to-end, requires no complicated pre-processing steps and the trained model is computationally efficient during testing, paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments. Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution.https://www.sciopen.com/article/10.26599/BDMA.2022.9020006deep learningvisual signaturescamera identificationconvolutional neural networksdeep feature learning |
| spellingShingle | Chaity Banerjee Tharun Kumar Doppalapudi Eduardo Pasiliao Tathagata Mukherjee Deep Feature Learning for Intrinsic Signature Based Camera Discrimination Big Data Mining and Analytics deep learning visual signatures camera identification convolutional neural networks deep feature learning |
| title | Deep Feature Learning for Intrinsic Signature Based Camera Discrimination |
| title_full | Deep Feature Learning for Intrinsic Signature Based Camera Discrimination |
| title_fullStr | Deep Feature Learning for Intrinsic Signature Based Camera Discrimination |
| title_full_unstemmed | Deep Feature Learning for Intrinsic Signature Based Camera Discrimination |
| title_short | Deep Feature Learning for Intrinsic Signature Based Camera Discrimination |
| title_sort | deep feature learning for intrinsic signature based camera discrimination |
| topic | deep learning visual signatures camera identification convolutional neural networks deep feature learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020006 |
| work_keys_str_mv | AT chaitybanerjee deepfeaturelearningforintrinsicsignaturebasedcameradiscrimination AT tharunkumardoppalapudi deepfeaturelearningforintrinsicsignaturebasedcameradiscrimination AT eduardopasiliao deepfeaturelearningforintrinsicsignaturebasedcameradiscrimination AT tathagatamukherjee deepfeaturelearningforintrinsicsignaturebasedcameradiscrimination |