While humans may have cornered the market on writing songs (including public safety inspired bangers), rhythm itself is more widespread across the animal kingdom. And some animals could have better rhythm than us Homo sapiens. One trained California sea lion (Zalophus californianus) named Ronan can keep a beat better than some people, according to a new study published May 1 in the journal Scientific Reports. In lab settings, other non-human animals have shown some ability to move in time to a range of regular rhythms. Some bird species can be very precise, but do not necessarily maintain that persistence over time.

Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement associated with these models. Therefore, it is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods are limited in their applicability to specific types of generative models and require additional steps during training or generation. This restricts their use with pre-trained models that lack these specific operations and may compromise the quality of image generation. To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for the generated images of the given model and other images. Based on our analysis, we propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images from a specific generative model and other images, including those generated by different models and real images.