Frequency-aware Correlation Discovering and Spatial Forgery Clue Distilling for Synthetic Image Detection

Recent text-to-image generative models facilitate creating vivid images with arbitrary contents that are indistinguishable from authentic ones by naked eyes. Despite progress in synthetic image detection, detecting the image from new generators remains challenging. Because advanced generators leave fewer visible forgery traces, while different generative frameworks produce varied forgery patterns. We notice that generative models consistently struggle with fine-detailed content generation, creating abnormal spatial dependencies among neighboring pixels in complex texture regions. In this paper, we propose a methodology of gazing local detail of forgery (GLDF) for generator agnostic synthetic image detection, which identifies prominent spatial dependencies to capture subtle forgery. Concretely, we design frequency-aware correlation discovering (FACD) module to learn dynamic filters by instance-adaptive frequency masking block for identifying prominent spatial deficiencies, which distributed in different spatial positions with various patterns. Furthermore, we introduce the spatial forgery clue distilling module (SFCD) to iteratively aggregate and refine spatial dependencies from different positions by spatial aggregating and prototype global interacting blocks. Extensive experiments demonstrate that GLDF outperforms state-of-the-art methods on detecting synthetic images from different generators.