UniGADD：通用型生成器无关的深度伪造检测器
UniGADD: Universal Generator-Agnostic Deepfake Detector
AI 导读
arXiv 论文提出 UniGADD，一种生成器无关的通用深度伪造检测方法，对已知生成器达到 SOTA 级准确率，对未见过的生成器显著优于现有方法。方法采用两阶段优化：先用对比损失学习真假图像的判别特征嵌入，再仅针对真实样本优化嵌入空间的簇间分离与簇内紧凑度，从而提升对未见深度伪造技术的泛化能力。
正文 · 原文
Abstract:In this paper, we introduce Universal Generator-Agnostic Deepfake Detector (UniGADD), a novel method that employs supervised learning to achieve high accuracy on known deepfake generators, while maintaining robust performance on previously unseen ones. The proposed approach follows a two-stage optimisation process. In the first stage, a contrastive loss encourages the model to learn discriminative feature embeddings from real and known fake images, resulting in strong performance within the training domain. In the second stage, the embedding space is refined by promoting inter-cluster separation and intra-cluster compactness, applied exclusively to real samples. This refinement enhances generalisability, enabling the method to exhibit improved robustness against unseen deepfake generation techniques. UniGADD achieves accuracy on par with state-of-the-art methods for known generators, while significantly outperforming them on unseen cases, demonstrating its scalability and practical applicability for adversarial content detection.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07647 [cs.CV] |
| (or arXiv:2611.07647v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07647 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Agil Aghasanli [view email]
[v1]
Thu, 5 Nov 2026 08:34:07 UTC (2,224 KB)
来源：arXiv cs.AI · arxiv.org