Wavelet-enhanced Weakly Supervised Local Feature Learning for Face Forgery Detection

Face forgery detection is getting increasing attention due to the security threats caused by forged faces. Recently, local patch-based approaches have achieved sound achievements due to effective attention to local details. However, there are still unignorable problems: a) local feature learning requires patch-level labels to circumvent label noise, which is not practical in real-world scenarios; b) the commonly used DCT (FFT) transform loses all spatial information, which brings difficulty in handling local details. To compensate for such limitations, a novel wavelet-enhanced weakly supervised local feature learning framework is proposed in this paper. Specifically, to supervise the learning of local features with only image-level labels, two modules are devised based on the idea of multi-instance learning: local relation constraint module (LRCM) and category knowledge-guided local feature aggregation module (CKLFA). LRCM constrains the maximum distance between local features of forged face images greater than that of real face images. CKLFA adaptively aggregates local features based on their correlation to global embedding containing global category information. Combining these two modules, the network is encouraged to learn discriminative local features supervised only by image-level labels. Besides, a multi-level wavelet-powered feature enhancement module is developed to promote the network mining local forgery artifacts from spatio-frequency domain, which is beneficial to learning discriminative local features. Extensive experiments show that our approach outperforms previous state-of-the-art methods when only image-level labels are available and achieves comparable or even better performance than counterparts using patch-level labels.