Gardella Marina, Musé Pablo, Morel Jean-Michel, Colom Miguel
Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, CNRS, 91190 Gif-sur-Yvette, France.
IIE, Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.
J Imaging. 2021 Jul 17;7(7):119. doi: 10.3390/jimaging7070119.
A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.
从获取原始图像的那一刻起，就要应用一个复杂的处理链，直到获得最终图像。这个过程将原本呈泊松分布的噪声转化为一个复杂的噪声模型。噪声不一致性分析是伪造检测的丰富来源，因为伪造区域可能经历了不同的处理流程或相机外处理。我们提出了一种多尺度方法，该方法被证明适用于分析JPEG压缩图像中存在的高度相关噪声。我们在每个颜色通道和每个尺度上为每个图像块估计一条噪声曲线。然后，我们通过计算局部噪声曲线中低于全局噪声曲线的区间百分比，将每条噪声曲线与其从整个图像中获得的对应噪声曲线进行比较。由于许多伪造行为会造成局部噪声不足，因此该过程会产生关键的检测线索。我们的方法被证明与现有技术具有竞争力。在使用MCC分数进行评估时，或者在足够大的伪造区域以及针对颜色化攻击进行评估时，无论采用何种评估指标，它都优于所有其他方法。
