The analysis of video compression history is one of the important issues in video forensics. It can assist forensics analysts in many ways, e.g., to determine whether a video is original or potentially tampered with, or to evaluate the real quality of a re-encoded video, etc. In the existing literature, however, there are very few works targeting videos in HEVC format (the most recent standard), especially for the issue of the detection of transcoded videos. In this paper, we propose a novel method based on the statistics of Prediction Units (PUs) to detect transcoded HEVC videos from AVC format. According to the analysis of the footprints of HEVC videos, the frequencies of PUs (whether in symmetric patterns or not) are distinguishable between original HEVC videos and transcoded ones. The reason is that previous AVC encoding disturbs the PU partition scheme of HEVC. Based on this observation, a 5D and a 25D feature set are extracted from I frames and P frames, respectively, and are combined to form the proposed 30D feature set, which is finally fed to an SVM classifier. To validate the proposed method, extensive experiments are conducted on a dataset consisting of CIF (352 × 288) and HD 720p videos with a diversity of bitrates and different encoding parameters. Experimental results show that the proposed method is very effective at detecting transcoded HEVC videos and outperforms the most recent work.