LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection

Abstract

This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Net-work (LAA-Net). Existing methods for high-quality deep-fake 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 ma-nipulations. 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 strate-gies, 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 ef-fective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy. Experiments performed on sev-eral benchmarks show the superiority of our approach in terms of Area Under the Curve (AUC) and Average Preci-sion (AP). The code is available at https://github.com/10Ring/LAA-Net.