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.

翻译：图像与视频生成模型生成逼真图像的能力已达到前所未有的高度，使得在许多情况下难以区分真实图像与伪造图像。然而，尽管取得了这一进展，生成图像与真实世界图像之间仍存在质量差距。为此，我们广泛梳理了学术出版物与社交媒体中的大量文献，识别出图像生成模型存在的质性不足，并将其分为五大类别。通过理解这些缺陷，我们能够明确模型需要改进的方向，同时制定检测深度伪造的策略。深度伪造在当今社会的泛滥令人担忧，我们的研究发现有助于减轻其负面影响。