Self-Evolving Experts for Continual Test-Time Adaptation in AI-Generated Image Detection

AI-generated image detectors are typically trained once and kept static after deployment, making them vulnerable to emerging generators with unseen forensic traces. While continual test-time adaptation (CTTA) is a promising direction, effective self-training requires reliable pseudo-supervision and stable knowledge consolidation across distributions. We propose SEED (Self-Evolving Experts for Detection), a framework that enables pretrained detectors to adapt continually via a dynamically managed pool of residual experts, in the absence of ground-truth labels, generator identities, or domain boundaries. To this end, SEED dynamically manages its expert pool through shift detection for expert creation and decision-orthogonal expert routing. It creates a new expert only when the detector's scores show a persistent change, while using decision-orthogonal features to reuse existing experts when a context recurs. Each expert accumulates feature statistics weighted by the reliability of its pseudo-labels, and updates its residual head through balanced feature replay. Across four public benchmarks, SEED outperforms SOTA methods and improves final accuracy by up to 17.42 percentage points over the frozen detector without CTTA. All code and data will be publicly available upon acceptance.