Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of a potent detector requires knowledge of the type of manipulation, something that cannot be known ( a priori ). Thus, the latest effort is directed towards developing model-free (i.e., generalized) detectors capable of detecting multiple manipulation types. In this article, we propose a novel detector capable of exposing seven different manipulation types in low-resolution compressed images. Our proposed approach is based on a two-layer convolutional neural network (CNN) to extract frequency domain features of image median filtered residual that are classified using two different classifiers—softmax and extremely randomized trees. Extensive experiments demonstrate the efficacy of proposed detector over existing state-of-the-art detectors.

中文翻译：

---

使用图像残差频域特征的基于 CNN 的多重操作检测器

越来越复杂的图像编辑工具使修改图像变得容易。通常，这些修改是精心设计的、令人信服的，并且即使是仔细的人工检查也无法检测到。这些考虑促使开发取证算法和方法来检测对图像所做的修改。然而，这些检测器是模型驱动的（即，特定于操作的），并且选择有效的检测器需要了解操作类型，而这是无法知道的（先验）。因此，最新的努力是针对开发能够检测多种操作类型的无模型（即广义）检测器。在本文中，我们提出了一种新型检测器，能够在低分辨率压缩图像中暴露七种不同的操作类型。我们提出的方法基于两层卷积神经网络（CNN）来提取图像中值滤波残差的频域特征，这些特征使用两个不同的分类器——softmax 和极端随机树进行分类。大量实验证明了所提出的检测器对现有最先进检测器的有效性。