Hierarchical Channel Stacking: A Structured Decision Framework for AI-Generated Image Detection
Many synthetic-image detectors produce accurate predictions but offer limited insight into how those decisions are formed. This paper introduces Hierarchical Channel Stacking (HCS), a compact framework for AI-generated image detection that converts intermediate CNN activations into a structured 60-dimensional representation organized across three progressively deeper backbone stages. HCS uses per-channel Level-1 classifiers and a Level-2 aggregator to produce image-level predictions while preserving explicit hierarchical structure for analysis. On a benchmark spanning GAN and diffusion generators, HCS achieves 86.7% accuracy and 86.7% macro-F1 on the held-out test set. Stage ablation show...
| Authors | Saifullah Shoaib, Akash Borigi, Rupendra Lekkala et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 August 2026 |
| Last updated at source | 2 September 2026 |
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