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 is available at https://github.com/10Ring/LAA-Net.

翻译：本文提出了一种用于高质量深度伪造检测的新方法，称为局部伪影注意力网络（LAA-Net）。现有的高质量深度伪造检测方法主要基于监督式二元分类器与隐式注意力机制的结合。因此，它们对未见过的篡改操作泛化能力不足。为解决此问题，本文做出了两项主要贡献。首先，提出了一种在多任务学习框架内的显式注意力机制。通过结合基于热图的自注意力与自一致性注意力策略，LAA-Net被强制聚焦于少数几个易于出现伪影的脆弱小区域。其次，提出了一种增强型特征金字塔网络（E-FPN），作为一种简单而有效的机制，将具有判别性的低层特征传播至最终的特征输出，并具有限制冗余的优势。在多个基准数据集上进行的实验表明，我们的方法在曲线下面积（AUC）和平均精度（AP）方面均具有优越性。代码发布于 https://github.com/10Ring/LAA-Net。