Search papers, labs, and topics across Lattice.
This paper introduces Hierarchical Channel Stacking (HCS), a novel framework for detecting AI-generated images that leverages a structured 60-dimensional representation derived from intermediate CNN activations across three backbone stages. HCS achieves impressive performance with 86.7% accuracy and macro-F1 on benchmark datasets, while also providing insights into the decision-making process of synthetic-image detectors. The hierarchical structure not only enhances predictive accuracy but also reveals distinct contribution profiles for different image generation methods, underscoring the framework's utility for both detection and analysis.
HCS not only detects AI-generated images with high accuracy but also reveals how different generation methods influence detection decisions through a structured analysis of CNN activations.
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 shows that the full three-stage system outperforms reduced single-stage and two-stage variants, indicating that the hierarchy carries complementary predictive information. Stage-level contribution analysis further shows that, in the analyzed detector setting, fake GAN and fake diffusion images exhibit distinct stage-level contribution profiles. These results position HCS not simply as a compact detector, but as a structured framework for studying how synthetic-image detectors assemble evidence across representation levels.
