English(EN) Scalable Black-Box Model Attribution for Images

新的黑盒方法可准确归因生成式图像模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-18 04:00

研究人员开发了一种名为 Raw-Patch Attribution (RPA) 的新方法，用于识别生成给定图像的模型。该技术利用轻量级卷积神经网络 (CNN)，并在严格的黑盒设置下有效运行，这意味着它不需要访问生成模型的内部工作原理。与先前的方法相比，RPA 表现出更高的准确性和效率，能够以高精度成功归因 DRAGON 和 OpenFake 等模型。此外，RPA 提取的特征还可以用于其他任务，例如追踪模型谱系、对未见过的生成器进行分组以及用最少的数据适应新模型。 AI

影响提供了一种更有效、更准确的方法来识别生成图像的来源，有助于内容溯源和检测。

排序理由详细介绍新模型归因技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍新模型归因技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Asaf Livne, Amir Jevnisek, Shai Avidan · 2026-08-18 04:00

可扩展的图像黑盒模型归因

arXiv:2608.15652v1 Announce Type: new Abstract: The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existing methods have grown as elaborate as the generators they target, on the as- sum…

本文如何被排名

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可扩展的图像黑盒模型归因

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