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| Format: | Recurso digital |
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Zenodo
2024
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18501209 |
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| _version_ | 1866902239759564800 |
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| author | Reed, Alex |
| author_facet | Reed, Alex |
| contents | <p>A genuine raw image is a measurement of photon to electron conversion on silicon, not merely a container file with camera tags. This paper presents a physics based forensic method that tests whether the active image payload is electrically coupled to the Optical Black reference pixels on the same sensor die, detecting “re-wrapped" or "injected" payloads, which is the core threat model. We define the Voltage Dead Zone as a statistically significant gap between the Optical Black baseline distribution and the darkest values in the active image area when evaluated in linear raw space with automatic scaling disabled. We also define histogram indicators that detect algorithmic noise injection, including Gaussian Sterility where the floor noise distribution is an unusually smooth Gaussian with suppressed heavy tails. For exposures captured at standard ISOs, where analog gain is low and dynamic range is high, the joint presence of a large Voltage Dead Zone and Gaussian Sterility provides a strong and testable basis to conclude that an image set is synthetic or that an external payload has been injected into a raw container.</p> <p>Independent forensic results confirming that these 19 "cloud images" originated from no known CMOS sensor further validate the robustness of physics-based sterility in forgery detection.</p> <p><a name="bookmark=id.30j0zll"></a><em>Index Terms</em>—CMOS image sensors, digital forensics, image authentication,, optical black, sensor noise, synthetic media detection.</p> <p>Version 3.11; Feb 2 2026: Addendum iincluded</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18501209 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Forensic Authentication of RAW Imagery Using Optical Black and CMOS Sensor Noise Analysis Reed, Alex CMOS image sensors, digital forensics, image authentication, Canon, optical black, sensor noise, synthetic media detection. <p>A genuine raw image is a measurement of photon to electron conversion on silicon, not merely a container file with camera tags. This paper presents a physics based forensic method that tests whether the active image payload is electrically coupled to the Optical Black reference pixels on the same sensor die, detecting “re-wrapped" or "injected" payloads, which is the core threat model. We define the Voltage Dead Zone as a statistically significant gap between the Optical Black baseline distribution and the darkest values in the active image area when evaluated in linear raw space with automatic scaling disabled. We also define histogram indicators that detect algorithmic noise injection, including Gaussian Sterility where the floor noise distribution is an unusually smooth Gaussian with suppressed heavy tails. For exposures captured at standard ISOs, where analog gain is low and dynamic range is high, the joint presence of a large Voltage Dead Zone and Gaussian Sterility provides a strong and testable basis to conclude that an image set is synthetic or that an external payload has been injected into a raw container.</p> <p>Independent forensic results confirming that these 19 "cloud images" originated from no known CMOS sensor further validate the robustness of physics-based sterility in forgery detection.</p> <p><a name="bookmark=id.30j0zll"></a><em>Index Terms</em>—CMOS image sensors, digital forensics, image authentication,, optical black, sensor noise, synthetic media detection.</p> <p>Version 3.11; Feb 2 2026: Addendum iincluded</p> |
| title | Forensic Authentication of RAW Imagery Using Optical Black and CMOS Sensor Noise Analysis |
| topic | CMOS image sensors, digital forensics, image authentication, Canon, optical black, sensor noise, synthetic media detection. |
| url | https://doi.org/10.5281/zenodo.18501209 |