LAA-Net: Localized Artifact Attention Network for High-Quality Deepfakes
  Detection
Dat Nguyen Nesryne Mejri Inder Pal Singh Polina Kuleshova Marcella Astrid Anis Kacem Enjie Ghorbel Djamila Aouada
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.