TokenTrace: 通过水印标记令牌恢复实现多概念归因
TokenTrace: Multi-Concept Attribution through Watermarked Token Recovery
AI总结：
本文提出TokenTrace框架，通过在语义域中嵌入秘密签名，实现对生成图像中多个概念的鲁棒归因，实验表明其在单概念和多概念归因任务中均优于现有方法。
摘要：
生成式AI模型对知识产权构成重大挑战，因其能复制独特艺术风格和概念而不进行归因。尽管水印技术提供了解决方案，但现有方法在复杂场景中（如图像中包含多个概念）往往失效。本文引入TokenTrace，一种新型主动水印框架，通过同时扰动文本提示嵌入和初始潜在噪声，嵌入秘密签名。我们提出基于查询的TokenTrace模块，输入生成图像和文本查询（如特定对象或风格），以解耦和独立验证多个概念。实验表明，该方法在单概念和多概念归因任务中均达到最先进的性能，显著优于现有基线，同时保持高视觉质量和对常见转换的鲁棒性。
英文摘要：
Generative AI models pose a significant challenge to intellectual property (IP), as they can replicate unique artistic styles and concepts without attribution. While watermarking offers a potential solution, existing methods often fail in complex scenarios where multiple concepts (e.g., an object and an artistic style) are composed within a single image. These methods struggle to disentangle and attribute each concept individually. In this work, we introduce TokenTrace, a novel proactive watermarking framework for robust, multi-concept attribution. Our method embeds secret signatures into the semantic domain by simultaneously perturbing the text prompt embedding and the initial latent noise that guide the diffusion model's generation process. For retrieval, we propose a query-based TokenTrace module that takes the generated image and a textual query specifying which concepts need to be retrieved (e.g., a specific object or style) as inputs. This query-based mechanism allows the module to disentangle and independently verify the presence of multiple concepts from a single generated image. Extensive experiments show that our method achieves state-of-the-art performance on both single-concept (object and style) and multi-concept attribution tasks, significantly outperforming existing baselines while maintaining high visual quality and robustness to common transformations.
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。
