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 c...
| Authors | Yijie Lin, Ching-Chun Chang, Isao Echizen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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