Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs in OSN image sharing. However, OSN image privacy itself is quite complicated, and solutions currently in place for privacy management in reality are insufficient to provide personalized, accurate and flexible privacy protection. A more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a survey of "privacy intelligence" that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present a definition and a taxonomy of OSN image privacy, and a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and the privacy issues associated with these behaviors. Then a systematic review on the representative intelligent solutions targeting those privacy issues is conducted, also in a stage-based manner. The resulting analysis describes an intelligent privacy firewall for closed-loop privacy management. We also discuss the challenges and future directions in this area.