Evidence Is All You Need—But Not Forever: Open-World Continual Image Forensics

Image forensics underpins multimedia security. Research is moving from detection under fixed conditions toward open-world forensics, where generators and image processing conditions continually change. Their joint effects can alter how visual features distinguish real from fake images, so past decision rules may become unreliable. Broader training coverage and continual updates improve detection. However, a theoretical gap remains: what should representations retain to reduce label requirements for future adaptation as decision rules change? To address this gap, we propose a new research framework and establish key theoretical guarantees. In a Gaussian model, we prove that coordinates learned from finite history reduce adaptation labels compared with retaining optimal past decision rules alone. These coordinates retain information that past decisions omit and future learning can use. Our bounds separately account for learning the coordinates from history and reaching a specified detection quality with current labels. We establish shared-space sufficiency, identifying when a shared space preserves full-space optimal AUROC as generation and processing change together. These results motivate Continual Updating of Evidence (CUE). With its visual encoder and initial detector fixed, CUE estimates within-class variation from historical and current statistics; current labels determine the class difference. One statistical objective yields the coordinates and classifier. We bound how statistical estimation error affects the classifier. Adding or subtracting window statistics supports continual learning and unlearning. Verified labels separately determine when fixed score contributions are suspended or restored. With 16 labels per class, CUE achieves state-of-the-art AUROC against 22 pretrained detectors across six standard benchmarks. CUE further turns accumulated experience into more data-efficient adaptation to new conditions. Together, these results establish a learning-theoretic foundation for open-world forensics.