Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity

Video frame-rate up-conversion is one of the common operations for tampering digital videos in the temporal domain, such as creating fake high-quality videos and splicing two video clips with different frame rates. However, few existing works have been proposed for detecting this form of tampering operation. Based on the analysis of extensive experiments, we found that frame-rate up-conversion algorithms employed in most current video editing softwares will inevitably introduce some periodic artifacts into inter-frame similarity in the resulting video frame sequence. By analyzing such artifacts, we propose a simple yet very effective method to expose video after frame-rate up-conversion, and further estimate its original frame rate. The experimental results evaluated on 100 original videos at different frame rates have shown the effectiveness of the proposed method. The average detection accuracy can achieve as high as 99 % on noise-free videos in uncompressed and H.264/AVC formats. Besides, the proposed method is robust to noise as the detection accuracy could reach over 85 % and 95 % on noised videos with Gaussian white noise when SNR is 33 db and 36 db respectively.