Human activities in the sea, such as intensive fishing and exploitation of offshore wind 1 farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, 2 surveying, and tracking of marine animals are often performed on the sites where those activities 3 take place. Nowadays, thank to high resolution cameras and to the development of machine 4 learning techniques, tracking of wild animals can be performed remotely and the analysis of the 5 acquired images can be automatized using state-of-the-art object detection models. However, 6 most state-of-the-art detection methods require lots of annotated data to provide satisfactory 7 results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome 8 and time consuming task, we focus in this article on the weakly supervised detection of marine 9 animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.