A Novel Adaptive DCT Filter for Image Manipulation Localization

Image Manipulation Localization (IML) is a fundamental binary segmentation task, focused on the precise identification and demarcation of the manipulated regions within an image. Most existing models primarily rely on RGB and noise features to accurately identify the tampered areas in images. However, in practical IML tasks, the effectiveness of commonly used noise feature modules is often compromised by the unknown tampering methods and the diversity of images. In this paper, we design an adaptive filter based on the Discrete Cosine Transform (DCT) to localize the manipulated regions. Furthermore, we theoretically demonstrate that the adaptive filter is equivalent to convolving the image with a large-scale convolutional kernel, thereby taking full account of features across the entire image. Through extensive experimentation on several benchmark image tampering datasets, our model has demonstrated performance that rivals the most state-of-the-art methods.