Adaptive Multi-Generator Deep Learning for AI-Generated Image Detection

Abstract

Rapid progress in generative text-to-image architectures has significantly narrowed the perceptual gap between machine-generated visuals and authentic camera captures. Such rapid proliferation introduces critical vulnerabilities across digital forensics, automated content filtering, identity verification, and public media trust. A recurrent shortcoming across contemporary forensic classifiers is their narrow domain specificity: while they reliably identify synthetic images produced by architectures present within their training distributions, classification accuracy deteriorates when analysing samples from unfamiliar or recently introduced engines. Option 1 (Methodology-focused and precise): This study reviews key developments in multi-generator detection and presents an adaptive framework that pairs specialized CNN backbones with transfer learning, dynamic gating weights, and systematic degradation testing. Synthesizing peer-reviewed benchmark findings from 2023 through 2026 as an empirical foundation, this study focuses on architectural formulation rather than reporting an unverified experimental trial. Moving beyond conventional gated-expert designs, the proposed system incorporates broader generator clustering, systematic resistance to image degradation, calibrated prediction confidence, and dedicated out-of-distribution benchmarks. This strategy aims to attenuate single-generator overfitting, reinforce real-world resilience, and outline an efficient route toward modular deployment. Core data demands, operational limitations, and key research horizons are likewise addressed