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.
翻译:为了打击恶意使用图像编辑技术,有必要对图像进行检测和本地化的图像操纵。 因此,有必要通过分析图像中的内在统计数据来区分真实和被篡改的区域。 我们侧重于JPEG压缩图像获取和编辑过程中留下的文物。 我们提议建立一个神经神经网络(CNN),使用离散的连线变异系数(即保留压缩文物的连线变异系数)实现图像操纵本地化。 标准CNN无法了解DCT系数的分布, 因为演进会丢弃了对DCT系数至关重要的空间坐标。 我们说明了如何设计和培训一个能够学习DCT系数分布的神经网络。 此外,我们引入了压缩艺术追踪网络(CAT-Net),共同使用图像采集工艺和压缩文物,大大超越了在发现和本地化被篡改区域以传统和深神经网络为基础的方法。