Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities. However, these capabilities also introduce significant security risks, such as deepfakes and copyright infringement. To mitigate these risks, a class of methods known as protective perturbation emerged, which prevents image misuse by injecting imperceptible adversarial noise.On the other hand, purification methods can effectively remove the protective perturbation, thereby exposing images again to the risk of malicious forgery.In this work, we formalize the anti-purification task, highlighting the challenges that existing approaches can not address properly, and propose a solution named AntiPure.AntiPure is robust against the "purification-customization'' workflow, owing to the two types of proposed guidance: 1) Patch-wise Frequency Guidance, which reduces the model’s influence over high-frequency components in the purified image, and 2) Erroneous Timestep Guidance, which disrupts the model’s denoising strategy across different timesteps.With additional guidance, AntiPure embeds imperceptible perturbation patterns resistant to purification, achieving effective output distortion after customization. Experiments show that our approach achieves minimal perceptual discrepancy, maximal distortion, and robust performance, outperforming current protective perturbation methods within the purification-customization workflow.