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<meta name="description" content="PRNU 相机可以简单的建模为\(I(x, y) = I_0(x, y) + \gamma I_0(x, y)K(x, y) + N(x, y)\)，其中为\(I_0\)无噪声的图像，\(y\)是叠加系数，\(N\)是其他噪声因素（灰尘，曝光时间过长等等），\(K\)就是本节中的相机噪声，更具体的指的是photo-response non-uniformity noise（PRNU）为光子（Cmos接受量）与电子（Cmos自有量）的相互干扰产生的一种具有特点性质的噪声。 PRNU提取 PRNU的提取比较简单, 首先提取总噪声\(W_k(x, y) = I_k(x, y) - \hat{I_k}(x …" />
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  <title>R08UST's Blog &ndash; 数字图像取证 - 基于相机属性的取证 3</title>

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    <h1 id="forensics_2_3">数字图像取证 - 基于相机属性的取证 3</h1>
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      Posted on Thu 01 October 2020 in <a href="/category/digital-image-forensics.html">Digital Image Forensics</a>

        &#8226; 1 min read
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    <h2>PRNU</h2>
<p>相机可以简单的建模为<span class="math">\(I(x, y) = I_0(x, y) + \gamma I_0(x, y)K(x, y) + N(x, y)\)</span>，其中为<span class="math">\(I_0\)</span>无噪声的图像，<span class="math">\(y\)</span>是叠加系数，<span class="math">\(N\)</span>是其他噪声因素（灰尘，曝光时间过长等等），<span class="math">\(K\)</span>就是本节中的相机噪声，更具体的指的是photo-response non-uniformity noise（PRNU）为光子（Cmos接受量）与电子（Cmos自有量）的相互干扰产生的一种具有特点性质的噪声。</p>
<h2>PRNU提取</h2>
<p>PRNU的提取比较简单, 首先提取总噪声<span class="math">\(W_k(x, y) = I_k(x, y) - \hat{I_k}(x, y)\)</span>. 随后便有
</p>
<div class="math">$$
K(x, y) = \frac{\sum^n_{k = 1}W_k(x, y)I_k(x, y)}{\sum^n_{k = 1}I^2_k(x, y)}
$$</div>
<h2>图像间的PRNU检测</h2>
<p>求解相关性算法, 可以检测两组图像是否使用同一相机进行拍摄.
</p>
<div class="math">$$
\rho = I(x, y)k(x, y)\bigotimes W(x, y)
$$</div>
<p>
其中<span class="math">\(\bigotimes\)</span>表示正则化的相关性</p>
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