用隐空间追踪生成图像来源，防篡改伪造。
Lost in Edits? A $λ$-Compass for AIGC Provenance
- 基于隐空间重建损失自适应校准，识别编辑痕迹。
- 在多轮文本/手动编辑下仍能准确溯源，优于基线方法。
- 无需修改生成流程，适合版权保护与可信生态。
扩散模型推动了文本引导图像编辑工具的发展，实现对合成内容的精准迭代修改。然而，这些工具普及后也带来滥用风险，亟需可靠的溯源方法保障内容真实性与可追溯性。现有方法在对抗性编辑场景下难以有效区分真实与篡改图像，尤其当多层编辑叠加时。本文提出 LambdaTracer，一种无需修改生成或编辑流程的隐空间溯源方法。通过自适应校准重建损失，该方法在自动化（如 InstructPix2Pix、ControlNet）和手动编辑（如 Adobe Photoshop）等多种迭代编辑场景下均表现稳健。大量实验表明，其在辨别恶意编辑图像方面持续优于基线方法，为开放、快速演进的 AIGC 生态提供切实可行的版权与信誉保护方案。
原文摘要 · Abstract (English)
Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content. However, as these tools become increasingly accessible, they also introduce significant risks of misuse, emphasizing the critical need for robust attribution methods to ensure content authenticity and traceability. Despite the creative potential of such tools, they pose significant challenges for attribution, particularly in adversarial settings where edits can be layered to obscure an image's origins. We propose LambdaTracer, a novel latent-space attribution method that robustly identifies and differentiates authentic outputs from manipulated ones without requiring any modifications to generative or editing pipelines. By adaptively calibrating reconstruction losses, LambdaTracer remains effective across diverse iterative editing processes, whether automated through text-guided editing tools such as InstructPix2Pix and ControlNet or performed manually with editing software such as Adobe Photoshop. Extensive experiments reveal that our method consistently outperforms baseline approaches in distinguishing maliciously edited images, providing a practical solution to safeguard ownership, creativity, and credibility in the open, fast-evolving AI ecosystems.
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