Detecting Diffusion-generated Images via Dynamic Assembly Forests
Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural networks (e.g., CNNs and Transformers), while largely overlooking the potential of traditional machine learning models. In this paper, we freshly investigate such alternatives and proposes a novel Dynamic Assembly Forest model (DAF) to detect diffusion-generated images. Built upon the deep forest paradigm, DAF addresses the inherent limitations in feature learning and scalable training, making it an effective diffusion-generated image detector. Compared to existing DNN-based methods, DAF has significantly fewer parameters, much lower computatio...
| Authors | Mengxin Fu, Yuezun Li |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 15 April 2026 |
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