Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection

Current AI-generated image detectors lack interpretability: Existing detection systems make binary real/fake decisions without providing clear, understandable evidence for their conclusions, making it difficult for users to understand why an image was flagged as synthetic.

Detectors ignore obvious visual flaws: The research found that current AI-generated image detectors "demonstrate negligible reliance on perceptual artifacts, even at the most basic distortion level"—meaning they often overlook obvious visual distortions and errors that humans can easily spot.

Models rely on hidden, unexplainable patterns: Even when trained to detect specific visual artifacts, AIGI detectors "substantially base their judgment on uninterpretable features," suggesting they're learning opaque patterns rather than genuine visual indicators of synthetic images.

Detailed annotations improve transparency and performance: The X-AIGD benchmark provides pixel-level categorization of artifacts across three levels (distortions, semantic errors, and logical inconsistencies), and the research shows that "explicitly aligning model attention with artifact regions can increase the interpretability and generalization of detectors."

Better benchmarks are essential for responsible deployment: The paper demonstrates that existing datasets lack sufficient detail and diversity in artifact coverage, highlighting the need for more comprehensive evaluation tools before AI-generated image detection systems can be reliably deployed in real-world applications.