Fusion Under Component Failure: Negative Results and Failure Modes in Ensemble AI-Generated Image Detection
We built an ordinary stacking ensemble for AI-generated image detection -- three open detectors producing five scores, fused by a gradient-boosted meta-learner that treats a detector's failure as missing data -- deployed it, and then evaluated it against three controls it should have faced first. This paper reports what the controls found, including where they overturned our own earlier conclusions. Fusion is worth its cost only when refitted on the target domain. The shipped meta-learner, fitted on a separate corpus, does not beat its best single member on 2000 StyleGAN faces (AUC 0.9896 vs 0.9961; McNemar p = 1.000). But a stacker refitted in-domain beats that member plus a post-hoc cal...
| Authors | Suraj Singh, Tushar Verma, Pragyan Singh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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