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

2026-02-06

AI Summary

This study addresses the performance limitations of existing monolithic models in unified cross-domain image forensic detection, which stem from feature space collapse caused by the heterogeneity of manipulation artifacts across subdomains. The work is the first to identify and formally characterize this issue, proposing a Semantic-Induced Constraint Adaptation (SICA) approach that leverages high-level semantic information as a structural prior to reconstruct a unified yet discriminative artifact feature space. Evaluated on the newly curated OpenMMSec multimodal forged image dataset, SICA effectively reorients the target feature space in an approximately orthogonal manner, significantly outperforming fifteen state-of-the-art methods within a single unified framework. This establishes the first high-performance monolithic paradigm for generalized forgery detection.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Fake Image Detection
• Monolithic Model
• Artifact Feature Space
• Unified-Yet-Discriminative
• Image Forensics

Innovation

• Monolithic Model
• Fake Image Detection
• Semantic-Induced Constrained Adaptation
• Artifact Feature Space
• Unified-Yet-Discriminative