arXiv cs.AI· Bernard Tomczyk, Agil Aghasanli, Plamen Angelov·· 11 小时前AI 评分32
UniGADD：通用生成器无关的深度伪造检测器
UniGADD: Universal Generator-Agnostic Deepfake Detector
AI 导读
UniGADD 是一种生成器无关的深度伪造检测方法，采用两阶段优化：第一阶段用对比损失从真实与已知伪造图像中学习判别性嵌入向量，第二阶段仅对真实样本强化簇间分离与簇内紧凑以提升泛化性。该方法在已知生成器上的准确率与 SOTA 方法持平，在未见过的生成器上显著优于它们。
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