Fast Latent-feature Augmentation for Cross-domain Face Forgery Detection

Face Forgery Detection (FFD) involves excavating more discriminative representations of face images, training recognition models to detect fake-faces, which is an important technology that ensures the authenticity, reliability, and security of facial information. However, the currently popular methods, due to excessive reliance on training data, only exhibit satisfactory detection performances within the same domain, but typically fail when applied to cross-domain scenarios. Therefore, how to achieve high generalization in cross-domain FFD with limited training-data has become a hot topic. Based on this, we propose the latent feature augmentation model for face forgery detection (LFAD) in cross-domain datasets. By constructing the Siamese Auto-Encoding Network, we can obtain label-consistent latent multi-view features, and introduce the contrast constraint to improve the discriminability of hard-samples. Furthermore, we simultaneously generate more diverse fake-features according to construction rules for high-generalization in efficiently training. Meanwhile, we induce the adaptive balance factor to avoid the imbalance category distribution caused by the generation of negative instances. Compared with other state-of-the-art methods, our model has not only achieved better performances in cross-domain and intra-domain detection, but also obtained faster detection efficiency. The code for our model has been open-sourced at: https://github.com/LNNU-computer-research-526/LFA-CDFD