The rapid development of Generative Models (GMs) for image synthesis poses challenges to identifying manipulated images accurately. Traditionally, detection methods are trained to identify image manipulations after they occur. However, these approaches often struggle to generalize to GMs not seen during training. Proactive methods have been introduced to address this limitation. In this paradigm, images are embedded with a protection template as a form of proactive defense. Previous methods exploited a finite set of templates to proactively counter image manipulation, however, this raises concerns about potential vulnerabilities since a finite number of templates can provide a predictable exploit for malicious attackers. This work presents a template-based detection system that is designed to generate personalized templates for each image. Our approach enhances protection while also improving the accuracy of manipulation detection. Our solution achieves high detection accuracy on unseen GMs while also outperforming existing solutions.