Chin-Chen Chang

Open access Jul 2026

Subband-Guided Hybrid Multi-Axis Attention Network for Frequency-Aware Image Super-Resolution

Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations while preserving structural information and high-frequency detail. Although the Hybrid Multi-Axis Network (HMA) effectively combines local and nonlocal attention, its shallow input representation still mixes low-frequency structure, directional detail, and noise-like high-frequency components. This study investigates whether an explicit frequency prior can be introduced before the HMA backbone without substantially increasing computational cost. Two discrete-wavelet-transform front-ends are examined under the ×2 setting. HMA-WSB uses lightweight subband-specific processing and weighted fusion before a shared HMA backbone, whereas HMA-MSB introduces asymmetric multi-subband branches and cross-band fusion. Evaluation includes the reported external 15-image experiment, selected-image pilots from Set5, Set14, BSD100, and Urban100, and supplementary medical and texture-domain samples. The results show small, content-dependent differences rather than a consistent reconstruction advantage: the proposed variants are slightly favorable on several images containing dense multidirectional detail, but the original HMA remains stronger on other natural, medical, and periodic-texture samples. Computational analysis on an NVIDIA GeForce RTX 5070 with a 64×64 low-resolution input shows that HMA-WSB increases measured inference latency by 1.637% with negligible parameter and memory overhead. HMA-MSB increases latency by 5.078%, parameter count by 1.463%, and estimated FLOPs by 0.489%. These findings indicate that wavelet-guided subband processing is compatible with HMA and that WSB provides the more computationally economical extension. However, because the standard-dataset evaluation is based on selected images and a complete component-level ablation is not available, the results should be interpreted as preliminary evidence of a content-dependent quality-cost trade-off rather than proof of broad superiority.

Ching-Chun Chang, Tzu-Chuen Lu, Chin-Chen Chang · Journal of Machine Learning... · 0 citations

#artificial intelligence Preprint Sep 2026

Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics

With the rapid proliferation of generative models on Machine Learning as a Service (MLaaS) platforms, reliably tracing the provenance of synthetic media without modifying generator architectures or parameters remains a major challenge. In this work, we propose a self-referential retrosynthesis framework for explainable AI provenance forensics under a fixed-generator setting. The framework leverages a jointly optimized encoder-decoder pair to implement a self-embedding mechanism that enables round-trip consistency verification. During inference, client inputs are first encoded and then processed by the generator to produce outputs with high visual fidelity. For forensic verification, the consistency between the resynthesized image and the query image is analyzed to determine whether the image originates from the target generative model. Our approach eliminates the need for watermark embedding or modifications to the generation process. Experimental results show that images generated from encoded inputs maintain visual quality comparable to original generator outputs, while decoded images reliably trace back to their corresponding source inputs. Furthermore, the framework provides interpretable evidence for generative content provenance, establishing a practical tool for explainable generative AI forensics.

Yijie Lin, Ching-Chun Chang, Isao Echizen et al. · 0 citations