Beyond Causal Models: Multi-Scale Linear Local Attention for AI-Generated Image Detection

Abstract

AI-generated content (AIGC) has become increasingly difficult to distinguish from real images, creating new challenges for media authentication. Existing detectors often rely on either convolutional networks, which focus on local patterns but lack global reasoning, or Transformers, which capture long-range context but suffer from high computational cost. Recent state space models such as Mamba provide linear-time processing, yet their causal structure leads to long-range dependency decay, making them less effective for detecting forgery clues that appear across distant image regions. In this work, we propose Multi-scale Linear Local Attention (MLLA), a unified framework for AIGC detection that combines local artifact modeling with efficient global context reasoning. Our design integrates an artifact-aware tokenization (AAT) with a Linear Local Attention (LLA) block that merges depthwise convolutions, linear attention, and rotary positional embedding to overcome the limitations of both Transformers and causal state space models. By stacking LLA blocks in a multi-scale encoder, the network learns fine-grained features in shallow layers and broader semantic clues in deeper layers. Experiments on a wide range of GAN and diffusion datasets show that MLLA achieves state-of-the-art performance and strong generalization to unseen generators. The results confirm that combining local priors with efficient non-causal global modeling is a simple yet powerful direction for AIGC detection.