MIDNet: multi-scale interaction and dynamic hard-sample mining for AI-generated image detection

Abstract

With the rapid advancement of generative artificial intelligence (AI), the visual fidelity of synthesized images has increased dramatically, posing serious challenges to the verification of digital content authenticity. Existing AI-generated image detection methods often suffer from limited generalization and robustness, particularly when confronting unknown generative models or complex post-processing perturbations. To attenuate such deficiency, we propose an AI-generated image detection scheme. Leveraging a frozen contrastive language–image pre-training with Vision Transformer as visual backbone, the network extracts and stacks multi-scale intermediate features from the transformer modules to effectively capture both low-level and high-level forensic fingerprints. Based on this representation, we introduce an improved convolutional block attention module, which adopts a cascaded design by first applying channel-wise attention and then spatial attention. This design enables the network to adaptively select informative feature hierarchies while strengthening the representation of local generative artifacts. To further optimize the feature space structure, we propose a hard-sample-aware contrastive learning loss. It dynamically mines hard samples to enhance intra-class compactness and inter-class separability. In addition, we construct a mixed-source training image dataset named mixed-source AI-generated image dataset, which covers diverse generative paradigms. Large-scale testing results show that our proposed scheme ranks first on average across seven benchmark datasets, with accuracy 82.96% and the area under receiver operating characteristic curve 93.43%, demonstrating its outstanding generalization ability. Code is publicly available at https://github.com/multimediaFor/MIDNet.