Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection

• URL: http://arxiv.org/abs/2608.00716v1
• Date: Sat, 01 Aug 2026 15:33:15 GMT
• Title: Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
• Abstract summary: Large-scale vision models (LVMs) pre-trained on web-scale datasets exhibit exceptional generalization power.<n>We introduce two detection methods: 1) data-free detection, which prunes model parameters to induce unlearning without data access, and 2) data-driven detection, which optimize LVMs to unlearn knowledge tied to generated images.
• Score: 107.62647907393377
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust detection of generated images is critical to counter the misuse of generative models. Existing methods primarily depend on learning from human-annotated training datasets, limiting their generalization to unseen distributions. In contrast, large-scale vision models (LVMs) pre-trained on web-scale datasets exhibit exceptional generalization power through exposure to diverse distributions, offering a transformative paradigm for this task. However, our experimental results reveal that LVMs pre-trained on natural-image-dominated data can effectively capture the features of both natural and generated images, yielding comparably low losses and thus limited discriminative capacity between them. This prompts a key question: When and how do LVMs exhibit different behaviors when capturing features of natural and generated images? This investigation reveals an insight: during unlearning, LVMs exhibit disparate forgetting dynamics with feature degradation for generated images escalating faster than natural ones. Inspired by the disparate dynamics, we introduce two detection methods: 1) data-free detection, which prunes model parameters to induce unlearning without data access, and 2) data-driven detection, which optimizes LVMs to unlearn knowledge tied to generated images. Extensive experiments conducted on various benchmarks demonstrate that our unlearning-based approach outperforms conventional detection methods. By recasting the detection task as a problem of machine unlearning, our work establishes a new paradigm for generated image detection.

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