Hybrid Semantic and Spectral Ensemble for Robust Synthetic Image Source Attribution
The rapid advancement of text-to-image (T2I) models has necessitated robust Synthetic Image Source Attribution (SIA) methodologies. A critical challenge in SIA is the distribution shift between pristine training images and real-world deployed images, which undergo unknown post-processing operations such as JPEG compression and blurring. In this work, proposed for the DLMMDD Challenge at ICANN 2026, we introduce a dual-branch ensemble framework fusing Semantic Deep Learning with Mathematical Forensic Feature Extraction. The semantic branch employs EfficientNet-B0 regularized with Exponential Moving Averaging (EMA) and Label Smoothing. The forensic branch extracts 126 mathematical features ...
| Authors | Md. Ajwad Hossain |
|---|---|
| Primary category | cs.CV |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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