Multi-Scale Wavelet Transformer for Face Forgery Detection

• URL: http://arxiv.org/abs/2210.03899v1
• Date: Sat, 8 Oct 2022 03:39:36 GMT
• Title: Multi-Scale Wavelet Transformer for Face Forgery Detection
• Authors: Jie Liu, Jingjing Wang, Peng Zhang, Chunmao Wang, Di Xie, Shiliang Pu
• Abstract summary: We propose a multi-scale wavelet transformer framework for face forgery detection. Frequency-based spatial attention is designed to guide the spatial feature extractor to concentrate more on forgery traces. Cross-modality attention is proposed to fuse the frequency features with the spatial features.
• Score: 43.33712402517951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection. Specifically, to take full advantage of the multi-scale and multi-frequency wavelet representation, we gradually aggregate the multi-scale wavelet representation at different stages of the backbone network. To better fuse the frequency feature with the spatial features, frequency-based spatial attention is designed to guide the spatial feature extractor to concentrate more on forgery traces. Meanwhile, cross-modality attention is proposed to fuse the frequency features with the spatial features. These two attention modules are calculated through a unified transformer block for efficiency. A wide variety of experiments demonstrate that the proposed method is efficient and effective for both within and cross datasets.

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