SEED：面向基于扩散模型的序列面部属性编辑的基准数据集
SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models
AI总结：
本文提出了大规模序列面部编辑数据集SEED，并开发了结合高频线索的Transformer模型FAITH，以解决扩散模型序列编辑的归因与检测鲁棒性挑战。
摘要：
扩散模型近期在广泛的语义属性上实现了精确且逼真的面部编辑。除了单步修改，越来越多的应用现在需要分析和跟踪渐进式编辑序列，例如头发、妆容或配饰的逐步变化。然而，序列编辑在编辑归因和检测鲁棒性方面引入了重大挑战，且缺乏专门为该任务定制的大规模、精细标注的基准，使问题进一步复杂化。我们引入了SEED，这是一个通过最先进的扩散模型构建的大规模序列编辑面部数据集。SEED包含超过90,000张具有一到四次序列属性修改的面部图像，使用多种基于扩散的编辑流程（LEdits、SDXL、SD3）生成。每张图像都标注了详细的编辑序列、属性掩码和提示词，以促进序列编辑跟踪、视觉溯源分析和篡改鲁棒性评估的研究。为了对该任务进行基准测试，我们提出了FAITH，这是一种频率感知的基于Transformer的模型，它结合高频线索来增强对细微序列变化的敏感性。包括对多种频域方法的系统比较在内的综合实验，证明了FAITH的有效性以及SEED带来的独特挑战。SEED为大规模研究渐进式基于扩散的编辑提供了具有挑战性且灵活的资源。数据集和代码将公开发布于：https://github.com/Zeus1037/SEED。
英文摘要：
Diffusion models have recently enabled precise and photorealistic facial editing across a wide range of semantic attributes. Beyond single-step modifications, a growing class of applications now demands the ability to analyze and track sequences of progressive edits, such as stepwise changes to hair, makeup, or accessories. However, sequential editing introduces significant challenges in edit attribution and detection robustness, further complicated by the lack of large-scale, finely annotated benchmarks tailored explicitly for this task. We introduce SEED, a large-scale Sequentially Edited facE Dataset constructed via state-of-the-art diffusion models. SEED contains over 90,000 facial images with one to four sequential attribute modifications, generated using diverse diffusion-based editing pipelines (LEdits, SDXL, SD3). Each image is annotated with detailed edit sequences, attribute masks, and prompts, facilitating research on sequential edit tracking, visual provenance analysis, and manipulation robustness assessment. To benchmark this task, we propose FAITH, a frequency-aware transformer-based model that incorporates high-frequency cues to enhance sensitivity to subtle sequential changes. Comprehensive experiments, including systematic comparisons of multiple frequency-domain methods, demonstrate the effectiveness of FAITH and the unique challenges posed by SEED. SEED offers a challenging and flexible resource for studying progressive diffusion-based edits at scale. Dataset and code will be publicly released at: https://github.com/Zeus1037/SEED.
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。
