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Face information in public images is vulnerable to tampering. Some studies have used pre-embedded watermarks to detect tampering but cannot recover the original face. To address this, we propose a proactive face hiding network that conceals face information in the background region for the first time. Our framework includes three U-Net-based modules: a preparation network, an encoder, and a recovery network. Special loss functions are designed to achieve our objective of recovering the original face from a protected image attacked by face swapping. In addition, we develop a neural network-based JPEG simulator and a differentiable simulator, offering a fresh perspective on addressing the robustness problem associated with JPEG compression. Our method generates protected images with a peak signal-to-noise ratio (PSNR) of 40.947 dB in experiments. Even after different face attacks, the recovered images maintain PSNR between 28.368 and 33.847 dB. After the attack of JPEG compression, the PSNR of the recovered image decreases by a maximum of 2.142 dB. Our scheme effectively generates high-quality protected images that resist face swapping and JPEG compression attacks, enabling recovery of the original faces.