
Text-based identity search is breaking down.
Names collide. Usernames rotate. Bios are rewritten. Handles are abandoned. But images persist.
A profile photo uploaded once can resurface years later on forums, corporate sites, archived pages, data brokers, and repost networks. Even when an account is deleted, its images often continue to travel. They are cached, resized, embedded, scraped, and redistributed across the public web.
This shift has changed how people search works. Discovery no longer begins with what someone writes about themselves. It begins with what they look like, what surrounds them, and where their images appear.
Visual OSINT, or open source intelligence based on images, reflects this reality. It combines reverse image search, computer vision, and contextual interpretation to move from a single photo to a broader understanding of an online identity.
This master guide brings together two dimensions of modern visual investigation. First, the practical workflow for finding where a photo appears and how it connects to profiles. Second, the advanced layer of AI-driven visual intelligence, including facial correlation, environmental analysis, and synthetic identity detection.








