不修改模型就能动态保护生成图像版权，防止侵权内容出现。
Guardians of Generation: Dynamic Inference-Time Copyright Shielding with Adaptive Guidance for AI Image Generation
- 生成时动态检测并干预，通过三模块自适应引导避让版权内容。
- 在多个模型上测试，版权内容生成减少且不影响图像质量与用户意图。
- 无需重训练，可直接接入现有扩散模型，适合实际部署场景。
现代文本到图像生成模型可能无意中复现训练数据中记忆的受版权保护内容，引发严重的侵权担忧。我们提出 Guardians of Generation，一种模型无关的推理时动态版权防护框架。该方法无需重新训练或修改生成模型权重，可无缝集成到现有扩散流水线中。其通过三个组件构成自适应引导机制：检测模块实时监控用户提示词和中间生成步骤，识别潜在版权内容特征；提示词重写模块在发现风险时动态转换提示词，去除或替换可能触发版权材料的表述，同时保持原意；自适应引导模块则调节扩散过程采样轨迹，主动规避已标记内容。三者协同形成稳健防护盾，实现创意保真度与版权合规性的可调平衡。我们在 Stable Diffusion、SDXL 与 Flux 等多种生成模型上验证了该方法，显著降低版权内容生成，且对输出保真度和用户意图对齐影响微乎其微。本工作提供了一种即插即用的实用防护方案，支持在真实版权约束下更负责任地部署生成模型。源代码地址：https://respailab.github.io/gog
原文摘要 · Abstract (English)
Modern text-to-image generative models can inadvertently reproduce copyrighted content memorized in their training data, raising serious concerns about potential copyright infringement. We introduce Guardians of Generation, a model agnostic inference time framework for dynamic copyright shielding in AI image generation. Our approach requires no retraining or modification of the generative model weights, instead integrating seamlessly with existing diffusion pipelines. It augments the generation process with an adaptive guidance mechanism comprising three components: a detection module, a prompt rewriting module, and a guidance adjustment module. The detection module monitors user prompts and intermediate generation steps to identify features indicative of copyrighted content before they manifest in the final output. If such content is detected, the prompt rewriting mechanism dynamically transforms the user's prompt by sanitizing or replacing references that could trigger copyrighted material while preserving the prompt's intended semantics. The adaptive guidance module adaptively steers the diffusion process away from flagged content by modulating the model's sampling trajectory. Together, these components form a robust shield that enables a tunable balance between preserving creative fidelity and ensuring copyright compliance. We validate our method on a variety of generative models such as Stable Diffusion, SDXL, and Flux, demonstrating substantial reductions in copyrighted content generation with negligible impact on output fidelity or alignment with user intent. This work provides a practical, plug-and-play safeguard for generative image models, enabling more responsible deployment under real-world copyright constraints. Source code is available at: https://respailab.github.io/gog
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