Adobe隐藏特征及其对传感器归因的影响
The Adobe Hidden Feature and its Impact on Sensor Attribution
AI总结：
本文研究了Adobe Lightroom和Photoshop在图像处理中引入的周期性128x128水印模式导致传感器归因假阳性的问题，并提出了两种移除水印的方法以防止假阳性。
摘要：
如果传感器指纹的提取如今已成为传感器归因的重要取证工具，最近的研究表明，来自多个传感器的图像由于存在共同的“泄漏”而更容易产生假阳性（FP）。在本文中，我们调查了这种泄漏的可能原因，在检查导致FP的源的EXIF元数据后，发现它们与Adobe Lightroom或Photoshop软件相关。对呈现FP的图像上残差之间的互相关揭示了周期性峰值，表明存在周期性模式。通过使用Adobe Lightroom自行处理图像，我们能够证明，从原始图像（或每通道16位编码图像）到8位编码图像的所有处理过程，都嵌入了一个与水印非常相似的周期性128x128模式。然而，我们也表明该水印取决于内容和用于处理图像的架构。论文的其余部分介绍了两种移除此水印的不同方法：一种是从图像噪声分量中移除，另一种是在像素域中移除。我们表明，对于呈现FP的相机，我们能够防止假阳性。与Adobe代表的讨论告知我们，公司决定添加此模式以引入抖动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
