Detecting and localizing image manipulation are necessary to counter
malicious use of image editing techniques. Accordingly, it is essential to
distinguish between authentic and tampered regions by analyzing intrinsic
statistics in an image. We focus on JPEG compression artifacts left during
image acquisition and editing. We propose a convolutional neural network (CNN)
that uses discrete cosine transform (DCT) coefficients, where compression
artifacts remain, to localize image manipulation. Standard CNNs cannot learn
the distribution of DCT coefficients because the convolution throws away the
spatial coordinates, which are essential for DCT coefficients. We illustrate
how to design and train a neural network that can learn the distribution of DCT
coefficients. Furthermore, we introduce Compression Artifact Tracing Network
(CAT-Net) that jointly uses image acquisition artifacts and compression
artifacts. It significantly outperforms traditional and deep neural
network-based methods in detecting and localizing tampered regions.

通过使用卷积神经网络 (CNN) 和离散余弦变换 (DCT) 系数，本文提出了一种用于本地化检测图像篡改的方法，并介绍了 Compression Artifact Tracing Network (CAT-Net)。CAT-Net 与传统方法和基于深度神经网络的方法相比，在检测和本地化篡改区域时具有更好的性能。