Deepfake techniques pose a significant threat to personal privacy and social security. To mitigate these risks, various defensive techniques have been introduced, including passive methods through fake detection and proactive methods through adding invisible perturbations. Recent proactive methods mainly focus on face manipulation but perform poorly against face swapping, as face swapping involves the more complex process of identity information transfer. To address this issue, we develop a novel privacy-preserving framework, named Anti-Fake Vaccine, to protect the facial images against the malicious face swapping. This new proactive technique dynamically fuses visual corruption and content misdirection, significantly enhancing protection performance. Specifically, we first formulate constraints from two distinct perspectives: visual quality and identity semantics. The visual perceptual constraint targets image quality degradation in the visual space, while the identity similarity constraint induces erroneous alterations in the semantic space. We then introduce a multi-objective optimization solution to effectively balance the allocation of adversarial perturbations generated according to these constraints. To further improving performance, we develop an additive perturbation strategy to discover the shared adversarial perturbations across diverse face swapping models. Extensive experiments on the CelebA-HQ and FFHQ datasets demonstrate that our method exhibits superior generalization capabilities across diverse face swapping models, including commercial ones.