This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection are mainly based on a supervised binary classifier coupled with an implicit attention mechanism. As a result, they do not generalize well to unseen manipulations. To handle this issue, two main contributions are made. First, an explicit attention mechanism within a multi-task learning framework is proposed. By combining heatmap-based and self-consistency attention strategies, LAA-Net is forced to focus on a few small artifact-prone vulnerable regions. Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy. Experiments performed on several benchmarks show the superiority of our approach in terms of Area Under the Curve (AUC) and Average Precision (AP). The code will be released soon.

翻译：本文提出了一种名为局部化伪影注意力网络（LAA-Net）的新型高质量深度伪造检测方法。现有高质量深度伪造检测技术主要基于带隐式注意力机制的监督式二分类器，导致其对未见过的篡改操作泛化能力不足。为解决该问题，本文作出两项主要贡献。首先，在多任务学习框架内提出显式注意力机制，通过结合热力图与自一致性注意力策略，强制LAA-Net聚焦于若干易产生伪影的关键局部区域。其次，提出增强型特征金字塔网络（E-FPN），作为一种简单有效的机制将判别性低层特征传播至最终特征输出，并具有限制冗余的优势。在多个基准数据集上的实验表明，本方法在曲线下面积（AUC）和平均精度（AP）指标上均具优越性。代码将择期开源。