Source Camera Identification Techniques: A Survey
The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/10/2/31 |
| _version_ | 1797297815227990016 |
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| author | Chijioke Emeka Nwokeji Akbar Sheikh-Akbari Anatoliy Gorbenko Iosif Mporas |
| author_facet | Chijioke Emeka Nwokeji Akbar Sheikh-Akbari Anatoliy Gorbenko Iosif Mporas |
| author_sort | Chijioke Emeka Nwokeji |
| collection | DOAJ |
| description | The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification. |
| first_indexed | 2024-03-07T22:26:47Z |
| format | Article |
| id | doaj.art-8a1c541a4ca54c92a9efd25a8dd9bb5d |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-07T22:26:47Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-8a1c541a4ca54c92a9efd25a8dd9bb5d2024-02-23T15:22:44ZengMDPI AGJournal of Imaging2313-433X2024-01-011023110.3390/jimaging10020031Source Camera Identification Techniques: A SurveyChijioke Emeka Nwokeji0Akbar Sheikh-Akbari1Anatoliy Gorbenko2Iosif Mporas3School of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds LS6 3QR, UKSchool of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds LS6 3QR, UKSchool of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds LS6 3QR, UKSchool of Physics, Engineering & Computer Science, University of Hertfordshire, Hertfordshire AL10 9AB, UKThe successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification.https://www.mdpi.com/2313-433X/10/2/31source camera identificationcamera brand source identificationcamera model source identificationsensor pattern noiseimage lens optical distortioncamera colour filter array |
| spellingShingle | Chijioke Emeka Nwokeji Akbar Sheikh-Akbari Anatoliy Gorbenko Iosif Mporas Source Camera Identification Techniques: A Survey Journal of Imaging source camera identification camera brand source identification camera model source identification sensor pattern noise image lens optical distortion camera colour filter array |
| title | Source Camera Identification Techniques: A Survey |
| title_full | Source Camera Identification Techniques: A Survey |
| title_fullStr | Source Camera Identification Techniques: A Survey |
| title_full_unstemmed | Source Camera Identification Techniques: A Survey |
| title_short | Source Camera Identification Techniques: A Survey |
| title_sort | source camera identification techniques a survey |
| topic | source camera identification camera brand source identification camera model source identification sensor pattern noise image lens optical distortion camera colour filter array |
| url | https://www.mdpi.com/2313-433X/10/2/31 |
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