PE-Mamba: Bidirectional Selective Layer Aggregation for AI-Generated Image Detection
AI-generated image (AIGI) detection has become increasingly challenging due to the rapid advancement of generative models and the diminishing gap between synthetic and authentic content. Existing vision transformer-based detectors commonly rely on weighted-sum strategies to aggregate intermediate representations across transformer layers, often overlooking the inherently ordered semantic progression of hierarchical features from shallow texture cues to deep semantic representations. In this work, we propose \textbf{PE-Mamba}, a novel framework built upon a pre-trained PE-Core vision transformer with lightweight LoRA adaptation that introduces three complementary components for cross-layer...
| Authors | Kutub Uddin, Nusrat Tasnim, Khalid Malik |
|---|---|
| Primary category | cs.CV |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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