Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but they also raise significant privacy concerns, particularly the risk of facial identity leakage. Recent defense mechanisms like Anti-DreamBooth attempt to mitigate this risk by injecting adversarial perturbations into user photos to prevent successful personalization. However, we identify two critical yet overlooked limitations of these methods. First, the adversarial examples often exhibit perceptible artifacts such as conspicuous patterns or stripes, making them easily detectable as manipulated content. Second, the perturbations are highly fragile, as even simple, non-learned filtering operations can effectively remove them, thereby restoring the model's ability to memorize and reproduce the user's identity. To investigate this vulnerability, we conduct a comprehensive evaluation of existing defenses under realistic purification threats, including both traditional image filters and adversarial purification methods. Our results reveal that none of the current methods maintains their protective effectiveness under such threats. These findings highlight that current defenses offer a false sense of security and underscore the urgent need for more imperceptible and robust protections to safeguard user identity in personalized generation.