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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Online Access: | https://arxiv.org/abs/2401.01366 |
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| _version_ | 1866910286122844160 |
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| author | Butora, Jan Bas, Patrick |
| author_facet | Butora, Jan Bas, Patrick |
| contents | If the extraction of sensor fingerprints represents nowadays an important forensic tool for sensor attribution, it has been shown recently that images coming from several sensors were more prone to generate False Positives (FP) by presenting a common "leak". In this paper, we investigate the possible cause of this leak and after inspecting the EXIF metadata of the sources causing FP, we found out that they were related to the Adobe Lightroom or Photoshop softwares. The cross-correlation between residuals on images presenting FP reveals periodic peaks showing the presence of a periodic pattern. By developing our own images with Adobe Lightroom we are able to show that all developments from raw images (or 16 bits per channel coded) to 8 bits-coded images also embed a periodic 128x128 pattern very similar to a watermark. However, we also show that the watermark depends on both the content and the architecture used to develop the image. The rest of the paper presents two different ways of removing this watermark, one by removing it from the image noise component, and the other by removing it in the pixel domain. We show that for a camera presenting FP, we were able to prevent the False Positives. A discussion with Adobe representatives informed us that the company decided to add this pattern in order to induce dithering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01366 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Adobe Hidden Feature and its Impact on Sensor Attribution Butora, Jan Bas, Patrick Cryptography and Security 68M25 I.4.1; I.4.10 If the extraction of sensor fingerprints represents nowadays an important forensic tool for sensor attribution, it has been shown recently that images coming from several sensors were more prone to generate False Positives (FP) by presenting a common "leak". In this paper, we investigate the possible cause of this leak and after inspecting the EXIF metadata of the sources causing FP, we found out that they were related to the Adobe Lightroom or Photoshop softwares. The cross-correlation between residuals on images presenting FP reveals periodic peaks showing the presence of a periodic pattern. By developing our own images with Adobe Lightroom we are able to show that all developments from raw images (or 16 bits per channel coded) to 8 bits-coded images also embed a periodic 128x128 pattern very similar to a watermark. However, we also show that the watermark depends on both the content and the architecture used to develop the image. The rest of the paper presents two different ways of removing this watermark, one by removing it from the image noise component, and the other by removing it in the pixel domain. We show that for a camera presenting FP, we were able to prevent the False Positives. A discussion with Adobe representatives informed us that the company decided to add this pattern in order to induce dithering. |
| title | The Adobe Hidden Feature and its Impact on Sensor Attribution |
| topic | Cryptography and Security 68M25 I.4.1; I.4.10 |
| url | https://arxiv.org/abs/2401.01366 |