论文标题
信号依赖性噪声的统计分析：在图像剪接伪造的盲定位中应用
Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery
论文作者
论文摘要
视觉噪声通常被视为图像质量的干扰，而它也可以为基于图像的法医任务提供关键的线索。通常，假定噪声包含一个添加剂高斯模型，以估算，然后用于揭示异常。但是，对于真实的传感器噪声，应将其建模为信号依赖性噪声（SDN）。在这项工作中，我们将SDN应用于剪接伪造本地化任务。通过对SDN模型的统计分析，我们假设可以将噪声建模为特定亮度的高斯近似值，并为噪声水平函数提出了可能性模型。通过构建最大的后马尔可夫随机场（MAP-MRF）框架，我们利用噪声的可能性来揭示剪接对象的外星区域，并具有概率组合完善策略。为了确保完全盲目的检测，采用迭代交替方法来估计MRF参数。实验结果表明，我们的方法有效，并提供了比较定位性能。
Visual noise is often regarded as a disturbance in image quality, whereas it can also provide a crucial clue for image-based forensic tasks. Conventionally, noise is assumed to comprise an additive Gaussian model to be estimated and then used to reveal anomalies. However, for real sensor noise, it should be modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing forgery localization tasks. Through statistical analysis of the SDN model, we assume that noise can be modeled as a Gaussian approximation for a certain brightness and propose a likelihood model for a noise level function. By building a maximum a posterior Markov random field (MAP-MRF) framework, we exploit the likelihood of noise to reveal the alien region of spliced objects, with a probability combination refinement strategy. To ensure a completely blind detection, an iterative alternating method is adopted to estimate the MRF parameters. Experimental results demonstrate that our method is effective and provides a comparative localization performance.
