保护你的知识产权：面向个性化生成的规模化来源追踪与归因
Protect-Your-IP: Scalable Source-Tracing and Attribution against Personalized Generation
AI总结：
针对个性化生成模型带来的版权侵权风险，提出融合主动水印与被动检测的统一来源追踪与归因方法，并采用增量学习提升对新模型的适应性，实验验证其有效性与抗遗忘性。
摘要：
随着个性化生成模型的出现，用户能够更轻易地创建与现有内容相似的图像，这加剧了侵犯肖像权和知识产权的风险。针对AI生成内容（AIGC）的传统事后检测与来源追踪方法采用主动水印技术，然而这些方法对个性化生成模型效果不佳。此外，AIGC的归因技术依赖于被动检测，但往往难以区分AIGC与真实图像，构成了重大挑战。将这两个过程整合到一个统一框架中，不仅满足了保护与取证的实际需求，还提升了归因任务的有效性。受此启发，我们提出了一种统一的图像版权来源追踪与归因方法，引入了一种融合主动与被动策略的创新性水印-归因方法。我们将版权水印嵌入受保护图像中，并训练一个水印解码器从个性化模型的输出中检索版权信息，将此水印作为确认图像是否由AIGC生成的初步步骤。为精确定位具体的生成技术，我们利用强大的视觉骨干网络进行分类。此外，我们实施了一种增量学习策略，以在不遗忘先前知识的情况下熟练地归因新的个性化模型，从而增强模型对新型生成方法的适应性。我们使用从网上获取的多种名人肖像系列进行了实验，结果证实了我们的方法在来源追踪和归因任务中的有效性，以及其对知识遗忘的鲁棒性。
英文摘要：
With the advent of personalized generation models, users can more readily create images resembling existing content, heightening the risk of violating portrait rights and intellectual property (IP). Traditional post-hoc detection and source-tracing methods for AI-generated content (AIGC) employ proactive watermark approaches; however, these are less effective against personalized generation models. Moreover, attribution techniques for AIGC rely on passive detection but often struggle to differentiate AIGC from authentic images, presenting a substantial challenge. Integrating these two processes into a cohesive framework not only meets the practical demands for protection and forensics but also improves the effectiveness of attribution tasks. Inspired by this insight, we propose a unified approach for image copyright source-tracing and attribution, introducing an innovative watermarking-attribution method that blends proactive and passive strategies. We embed copyright watermarks into protected images and train a watermark decoder to retrieve copyright information from the outputs of personalized models, using this watermark as an initial step for confirming if an image is AIGC-generated. To pinpoint specific generation techniques, we utilize powerful visual backbone networks for classification. Additionally, we implement an incremental learning strategy to adeptly attribute new personalized models without losing prior knowledge, thereby enhancing the model's adaptability to novel generation methods. We have conducted experiments using various celebrity portrait series sourced online, and the results affirm the efficacy of our method in source-tracing and attribution tasks, as well as its robustness against knowledge forgetting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
