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Forensic-Aware Continual Adaptation for Image Forgery Localization — arXiv:2609.38251

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38251
License (as archived): CC0-1.0
Archived: 2026-10-07T12:43:21.147Z
SHA-256 of the archived text: be59df57f0c7257bfa6a72c37bb87c59d13faec7811553ef0e016584592bde97
Stable URL of this page: https://metraipolis.com/documento/dc_76b02002c718

How to cite

Forensic-Aware Continual Adaptation for Image Forgery Localization — arXiv:2609.38251 — arXiv (en), https://arxiv.org/abs/2609.38251 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_76b02002c718 (Metraipolis archive dc_76b02002c718, sha256 be59df57f0c7257bfa6a72c37bb87c59d13faec7811553ef0e016584592bde97)

BibTeX

@misc{dc76b02002c718,
  title = {Forensic-Aware Continual Adaptation for Image Forgery Localization — arXiv:2609.38251},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38251},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_76b02002c718; sha256 be59df57f0c7257bfa6a72c37bb87c59d13faec7811553ef0e016584592bde97},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38251. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods can accurately localize manipulated regions but are often unable to adapt to newly emerging forgeries. In real-world forensic scenarios, data typically arrive sequentially, yet continual model adaptation remains largely unexplored in IFL. To bridge this gap, we introduce the first continual learning framework for IFL and establish a comprehensive benchmark under two realistic data-evolution protocols: cross-dataset and cross-content continual learning. Evaluations of representative state-of-the-art IFL and continual learning methods reveal substantial performance degradation, highlighting two key challenges: (1) adaptively capturing intrinsic forensic traces from incoming data across unseen domains, and (2) preserving previously acquired forensic knowledge during sequential adaptation. To address these challenges, we propose a forensic-aware continual adaptation framework. First, a forensic trace mining module employs Spatial Mixture-of-Forensic-Experts (SMoFE) to dynamically route complementary forensic cues across spatial locations, together with Forensic Evidence-Guided Dense Prompting (FEGDP) to transform low-level forensic traces into structured localization evidence for SAM. Second, Fisher-weighted LoRA Gradient (FLAG) surgery identifies old-task-sensitive adaptation directions and suppresses conflicting updates, mitigating catastrophic forgetting while preserving plasticity for emerging forgery domains. Extensive experiments demonstrate state-of-the-art performance in both pixel-level forgery localization and image-level forgery detection across diverse continual learning scenarios.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.AI).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.38251 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:14:13.836Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).