  Deep neural networks have significantly improved the performance of face
forgery detection models in discriminating Artificial Intelligent Generated
Content (AIGC). However, their security is significantly threatened by the
injection of triggers during model training (i.e., backdoor attacks). Although
existing backdoor defenses and manual data selection can mitigate those using
human-eye-sensitive triggers, such as patches or adversarial noises, the more
challenging natural backdoor triggers remain insufficiently researched. To
further investigate natural triggers, we propose a novel analysis-by-synthesis
backdoor attack against face forgery detection models, which embeds natural
triggers in the latent space. We thoroughly study such backdoor vulnerability
from two perspectives: (1) Model Discrimination (Optimization-Based Trigger):
we adopt a substitute detection model and find the trigger by minimizing the
cross-entropy loss; (2) Data Distribution (Custom Trigger): we manipulate the
uncommon facial attributes in the long-tailed distribution to generate poisoned
samples without the supervision from detection models. Furthermore, to
completely evaluate the detection models towards the latest AIGC, we utilize
both state-of-the-art StyleGAN and Stable Diffusion for trigger generation.
Finally, these backdoor triggers introduce specific semantic features to the
generated poisoned samples (e.g., skin textures and smile), which are more
natural and robust. Extensive experiments show that our method is superior from
three levels: (1) Attack Success Rate: ours achieves a high attack success rate
(over 99%) and incurs a small model accuracy drop (below 0.2%) with a low
poisoning rate (less than 3%); (2) Backdoor Defense: ours shows better robust
performance when faced with existing backdoor defense methods; (3) Human
Inspection: ours is less human-eye-sensitive from a comprehensive user study.
