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Recent advances in diffusion models have led to a quantum leap in the qualityof generative visual content. However, quantification of realism of the contentis still challenging. Existing evaluation metrics, such as Inception Score andFr\'echet inception distance, fall short on benchmarking diffusion models dueto the versatility of the generated images. Moreover, they are not designed toquantify realism of an individual image. This restricts their application inforensic image analysis, which is becoming increasingly important in theemerging era of generative models. To address that, we first propose a metric,called Image Realism Score (IRS), computed from five statistical measures of agiven image. This non-learning based metric not only efficiently quantifiesrealism of the generated images, it is readily usable as a measure to classifya given image as real or fake. We experimentally establish the model- anddata-agnostic nature of the proposed IRS by successfully detecting fake imagesgenerated by Stable Diffusion Model (SDM), Dalle2, Midjourney and BigGAN.  We further leverage this attribute of our metric to minimize an IRS-augmentedgenerative loss of SDM, and demonstrate a convenient yet considerable qualityimprovement of the SDM-generated content with our modification. Our effortshave also led to Gen-100 dataset, which provides 1,000 samples for 100 classesgenerated by four high-quality models. We will release the dataset and code.

On quantifying and improving realism of images generated with diffusion