提出新型注意力机制，检测高质量伪造人脸且泛化能力强
LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
- 设计显式局部伪影注意力，引导模型关注易出错区域
- 仅用真实与伪假样本训练，跨数据集性能媲美顶尖方法
- 适配CNN与Transformer，适合需要强泛化能力的研究者
本文提出统一的局部伪影注意力框架LAA-X，可有效检测高质量伪造人脸并具备对未见篡改方式的泛化能力。现有方法多依赖二分类器与隐式注意力，难以超越已知篡改类型。LAA-X采用多任务学习框架，结合基于混合的数据合成策略，设计辅助任务以引导模型聚焦于易产生伪影的局部区域。该框架兼容CNN与Transformer骨干网络，形成LAA-Net与LAA-Former两种版本。尽管仅在真实图像与伪假样本上训练，其在多个基准测试中表现媲美当前最优方法。代码与预训练权重已公开。
原文摘要 · Abstract (English)
In this paper, we propose Localized Artifact Attention X (LAA-X), a novel deepfake detection framework that is both robust to high-quality forgeries and capable of generalizing to unseen manipulations. Existing approaches typically rely on binary classifiers coupled with implicit attention mechanisms, which often fail to generalize beyond known manipulations. In contrast, LAA-X introduces an explicit attention strategy based on a multi-task learning framework combined with blending-based data synthesis. Auxiliary tasks are designed to guide the model toward localized, artifact-prone (i.e., vulnerable) regions. The proposed framework is compatible with both CNN and transformer backbones, resulting in two different versions, namely, LAA-Net and LAA-Former, respectively. Despite being trained only on real and pseudo-fake samples, LAA-X competes with state-of-the-art methods across multiple benchmarks. Code and pre-trained weights for LAA-Net\footnote{https://github.com/10Ring/LAA-Net} and LAA-Former\footnote{https://github.com/10Ring/LAA-Former} are publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。