{'fraction_non_alphanumeric': 0.020889487870619946, 'fraction_numerical': 0.0020215633423180594, 'mean_word_length': 5.4847161572052405, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fake-buster: a lightweight solution for deepfake detection

Recent advances in video manipulation techniques have made synthetic media creation more accessible than ever before. Nowadays, video edition is so realistic that we cannot rely exclusively on our senses to assess the veracity of media content. With the amount of manipulated videos doubling every six months, we need sophisticated tools to process the huge amount of media shared all over the internet, to remove the related videos as fast as possible, thus reducing potential harm such as fueling disinformation or reducing trust in mainstream media. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. Our method involves two networks: (1) a face identification network, extracting the faces contained in a video, and (2) a manipulation recognition network, considering the face as well as its neighbouring context to find potential artifacts, indicating that the face was manipulated. More particularly, we propose to make use of neural network compression techniques such as pruning and knowledge distillation to create a lightweight solution, able to rapidly process streams of videos. Our approach is validated on the DeepFake Detection Dataset, consisting of videos coming from 5 different manipulation techniques, reflecting the organic content found on the internet, and compared to state-of-the-art deepfake detection approaches.