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This paper introduces SPARED, an adversarial reinforcement learning framework designed to enhance the detection of AI-generated images by addressing the limitations of existing detectors. By employing a diffusion image editor to create increasingly challenging fake images and a reasoning MLLM to provide justifications for detection decisions, the framework ensures that both models evolve in tandem, effectively tackling the detector's blind spots. The results demonstrate a consistent improvement in detection accuracy across three external benchmarks, highlighting the framework's ability to adapt to the evolving landscape of image generation technologies.
The SPARED framework not only boosts detection accuracy but also enhances the quality of reasoning behind verdicts, making AI-generated image detection more robust and explainable.
Detecting AI-generated images is only half the task: a deployed detector must also justify its verdict, yet existing detectors inherit three failure modes from their training data: real and fake images collected from different sources invite provenance shortcuts, supervised explanation corpora teach templated rationales, and a static forgery corpus leaves the decision boundary standing still while generators keep moving. We introduce \methodname{}, an adversarial reinforcement learning framework that pits two heterogeneous models against each other. A diffusion image editor learns to edit real photographs into fake counterparts of those same photographs that fool the current detector, while a reasoning MLLM learns to expose them with a verdict grounded in free-form reasoning. Both rewards are shortcut-proof by design: the attacker is credited only when its edit is faithfully executed, and the defender only when its verdict is correct. As the two models alternate, each round's attacker regenerates a harder training pool aimed at the current detector's blind spots, so the detector must generalize rather than memorize any fixed artifact distribution. Although the explanation is never rewarded, its quality rises round over round as a side effect of accuracy-only training. A detector trained within this loop improves monotonically across rounds on each of three external benchmarks.
