Dual Contrastive Learning for General Face Forgery Detection


  • Ke Sun Xiamen University, China
  • Taiping Yao Tencent YouTu Lab
  • Shen Chen Tencent YouTu Lab
  • Shouhong Ding Tencent YouTu Lab
  • Jilin Li Tencent YouTu Lab
  • Rongrong Ji Xiamen University, China




Computer Vision (CV)


With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns. Previous works always formulate face forgery detection as a classification problem based on cross-entropy loss, which emphasizes category-level differences rather than the essential discrepancies between real and fake faces, limiting model generalization in unseen domains. To address this issue, we propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designed contrastive learning at different granularities to learn generalized feature representation. Concretely, combined with the hard sample selection strategy, Inter-Instance Contrastive Learning (Inter-ICL) is first proposed to promote task-related discriminative features learning by especially constructing instance pairs. Moreover, to further explore the essential discrepancies, Intra-Instance Contrastive Learning (Intra-ICL) is introduced to focus on the local content inconsistencies prevalent in the forged faces by constructing local region pairs inside instances. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art competitors. Our Code is available at https://github.com/Tencent/TFace.git.




How to Cite

Sun, K., Yao, T., Chen, S., Ding, S., Li, J., & Ji, R. (2022). Dual Contrastive Learning for General Face Forgery Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 2316-2324. https://doi.org/10.1609/aaai.v36i2.20130



AAAI Technical Track on Computer Vision II