Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction

• URL: http://arxiv.org/abs/2602.06676v1
• Date: Fri, 06 Feb 2026 13:03:26 GMT
• Title: Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
• Authors: Bo Du, Xiaochen Ma, Xuekang Zhu, Zhe Yang, Chaogun Niu, Jian Liu, Ji-Zhe Zhou,
• Abstract summary: monolithic Fake Image Detection (FID) models consistently yield inferior performance in practice.<n>We propose Semantic-Induced Constrained Adaptation (SICA), the first monolithic FID paradigm.<n>SICA outperforms 15 state-of-the-art methods and reconstructs the target unified-yet-discriminative artifact feature space.
• Score: 35.062003602486925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fake Image Detection (FID), aiming at unified detection across four image forensic subdomains, is critical in real-world forensic scenarios. Compared with ensemble approaches, monolithic FID models are theoretically more promising, but to date, consistently yield inferior performance in practice. In this work, by discovering the ``heterogeneous phenomenon'', which is the intrinsic distinctness of artifacts across subdomains, we diagnose the cause of this underperformance for the first time: the collapse of the artifact feature space driven by such phenomenon. The core challenge for developing a practical monolithic FID model thus boils down to the ``unified-yet-discriminative" reconstruction of the artifact feature space. To address this paradoxical challenge, we hypothesize that high-level semantics can serve as a structural prior for the reconstruction, and further propose Semantic-Induced Constrained Adaptation (SICA), the first monolithic FID paradigm. Extensive experiments on our OpenMMSec dataset demonstrate that SICA outperforms 15 state-of-the-art methods and reconstructs the target unified-yet-discriminative artifact feature space in a near-orthogonal manner, thus firmly validating our hypothesis. The code and dataset are available at:https: //github.com/scu-zjz/SICA_OpenMMSec.

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