From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images
The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of producing highly realistic forgeries, reinforcing the importance of multimedia forensics. Conventional approaches typically frame image manipulation detection as a binary classification task (real vs. generated), which limits the capability to distinguish and localize different forms of manipulation. To address these constraints, this work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered). In addition to classifying image authenticity, the framework incorporates a segmentation bran...
| Authors | Annalisa Gallina, Marco Fiorucci, Marco Brigo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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