ML Security PapersAggregating Diverse Cue Experts for AI-Generated Image Detection
Lei Tan 1,2, Shuwei Li 1, Mohan Kankanhalli 1, Robby T. Tan 1,2
Published on arXiv
2601.08790
Output Integrity Attack
OWASP ML Top 10 — ML09
Key Finding
MCAN outperforms the best prior method by up to 7.4% average accuracy on GenImage across eight different image generators.
MCAN (Multi-Cue Aggregation Network)
Novel technique introduced
The rapid emergence of image synthesis models poses challenges to the generalization of AI-generated image detectors. However, existing methods often rely on model-specific features, leading to overfitting and poor generalization. In this paper, we introduce the Multi-Cue Aggregation Network (MCAN), a novel framework that integrates different yet complementary cues in a unified network. MCAN employs a mixture-of-encoders adapter to dynamically process these cues, enabling more adaptive and robust feature representation. Our cues include the input image itself, which represents the overall content, and high-frequency components that emphasize edge details. Additionally, we introduce a Chromatic Inconsistency (CI) cue, which normalizes intensity values and captures noise information introduced during the image acquisition process in real images, making these noise patterns more distinguishable from those in AI-generated content. Unlike prior methods, MCAN's novelty lies in its unified multi-cue aggregation framework, which integrates spatial, frequency-domain, and chromaticity-based information for enhanced representation learning. These cues are intrinsically more indicative of real images, enhancing cross-model generalization. Extensive experiments on the GenImage, Chameleon, and UniversalFakeDetect benchmark validate the state-of-the-art performance of MCAN. In the GenImage dataset, MCAN outperforms the best state-of-the-art method by up to 7.4% in average ACC across eight different image generators.
Key Contributions
- Chromaticity Inconsistency (CI) cue: a novel representation that applies chromaticity transformation to expose camera-noise patterns absent in AI-generated images
- MCAN framework with Mixture-of-Encoder Adapter (MoEA) that dynamically integrates spatial, frequency-domain, and chromaticity cues into a unified feature space
- State-of-the-art results on GenImage, Chameleon, and UniversalFakeDetect benchmarks, outperforming best prior method by up to 7.4% average accuracy across eight generators
🛡️ Threat Analysis
Primary contribution is a novel AI-generated image detection framework (MCAN), directly addressing output integrity and content authenticity by distinguishing synthetic images from real ones across GAN and diffusion generators.