ArtGate: Injecting Fake Artifact Features into CLIP for AI-Generated Image Detection

With the rapid advancement of generative AI, synthetic images have become increasingly realistic, raising serious concerns regarding their potential misuse. Several prior methods detect AI-generated images by exploiting low-level fake artifacts left by generators. However, these fake artifacts are fragile and easily suppressed by post-processing (e.g., JPEG compression), leading detectors to misclassify fake images as real. To address this robustness limitation, we propose ArtGate, a novel AI-generated image detector that integrates a CLIP-ViT backbone with a frequency-domain artifact branch, and carefully design a confidence-aware gating mechanism to modulate the artifact branch. Specifically, the artifact branch employs wavelet analysis to extract artifact features from the high-frequency subbands of the image, while a confidence-aware gating mechanism selectively activates this branch only when generation-related fake artifacts are detected. After modulation by the gating mechanism, the artifact features are subsequently injected into the CLIP image encoder and adaptively fused with semantic features to enhance the detection performance. Extensive experiments demonstrate that ArtGate outperforms state-of-the-art methods in both generalization and robustness. Under random JPEG compression, our method achieves improvements of +5.88% in accuracy and +9.19% in F1-score on the AIGCDetectBenchmark.