  The ability of image and video generation models to create photorealistic
images has reached unprecedented heights, making it difficult to distinguish
between real and fake images in many cases. However, despite this progress, a
gap remains between the quality of generated images and those found in the real
world. To address this, we have reviewed a vast body of literature from both
academic publications and social media to identify qualitative shortcomings in
image generation models, which we have classified into five categories. By
understanding these failures, we can identify areas where these models need
improvement, as well as develop strategies for detecting deep fakes. The
prevalence of deep fakes in today's society is a serious concern, and our
findings can help mitigate their negative impact.
