With the popularity of image editing software and technology, it has become very easy to manipulate an image, which has challenged the authenticity and security of digital images. Digital Image Manipulation Forensics (DIMF) has become an important research direction to confirm the authenticity of images, detect the manipulations that images have undergone, and avoid the misuse of image editing. Currently, most DIMF targets the detection of multiple image manipulations with fixed parameters. However, we consider detecting image manipulation in a more complex scenario where the parameters are chosen to be arbitrary. In this paper, we propose a Feature Compensation Network (FCNet) based on non-uniform quantization of channels. Briefly, it contains three important parts: (1) feature enhancement block, which extracts and enhances valid information from low-level features and eliminates semantic gaps between them and the high-level features. (2) sensitivity estimation block, which obtains the importance coefficients of each channel and guides the non-uniform quantization of low-level features. (3) adaptive average pooling, which keeps the resolution of low-level features and high-level features consistent and ensures that subsequent feature fusion is appropriate. Through extensive experiments, we have demonstrated the effectiveness of the proposed method in detecting multiple image manipulations.

随着图像编辑软件和技术的普及，对图像进行操作变得非常容易，这对数字图像的真实性和安全性提出了挑战。数字图像操作取证 (DIMF) 已成为确认图像真实性、检测图像经过的操作以及避免图像编辑误用的重要研究方向。目前，大多数 DIMF 的目标是检测具有固定参数的多个图像操作。然而，我们考虑在更复杂的场景中检测图像操作，其中参数被选择为任意的。在本文中，我们提出了一种基于通道非均匀量化的特征补偿网络（FCNet）。简而言之，它包含三个重要部分：（1）特征增强块，它从低级特征中提取和增强有效信息，并消除它们与高级特征之间的语义差距。(2) 灵敏度估计块，获取每个通道的重要性系数，指导低层特征的非均匀量化。(3) 自适应平均池化，使低层特征和高层特征的分辨率保持一致，保证后续特征融合合适。通过广泛的实验，我们已经证明了所提出的方法在检测多个图像操作方面的有效性。(3) 自适应平均池化，使低层特征和高层特征的分辨率保持一致，保证后续特征融合合适。通过广泛的实验，我们已经证明了所提出的方法在检测多个图像操作方面的有效性。(3) 自适应平均池化，使低层特征和高层特征的分辨率保持一致，保证后续特征融合合适。通过广泛的实验，我们已经证明了所提出的方法在检测多个图像操作方面的有效性。