{"type":"ScholarlyArticle","title":"Dynamic Inconsistency-aware DeepFake Video Detection","authors":[{"name":"Ziheng Hu","slug":"ziheng-hu","given":"Ziheng","family":"Hu","affiliation":""},{"name":"Hongtao Xie","slug":"hongtao-xie","given":"Hongtao","family":"Xie","affiliation":""},{"name":"YuXin Wang","slug":"yuxin-wang","given":"Yuxin","family":"Wang","affiliation":""},{"name":"Jiahong Li","slug":"jiahong-li","given":"Jiahong","family":"Li","affiliation":""},{"name":"Zhongyuan Wang","slug":"zhongyuan-wang","given":"Zhongyuan","family":"Wang","affiliation":""},{"name":"Yongdong Zhang","slug":"yongdong-zhang","given":"Yongdong","family":"Zhang","affiliation":""}],"abstract":"The spread of DeepFake videos causes a serious threat to information security, calling for effective detection methods to distinguish them. However, the performance of recent frame-based detection methods become limited due to their ignorance of the inter-frame inconsistency of fake videos. In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies. The CRM contains two parallel branches. The first branch takes faces from adjacent frames as input, and calculates a structure similarity map for a global inconsistency representation. The second branch only focuses on the inter-frame variation of independent critical regions, which captures the local inconsistency. To the best of our knowledge, this is the first work to totally use the inter-frame inconsistency information from the global and local perspectives. Compared with existing methods, our model provides a more accurate and robust detection on FaceForensics++, DFDC-preview and Celeb-DFv2 datasets.","doi":"10.24963/ijcai.2021/102","url":"https://ijcai.org/proceedings/2021/102","pdf":"https://ijcai.org/proceedings/2021/0102.pdf","markdown":"https://ijcai.org/proceedings/2021/102.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/102","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Computer Vision I","track":"Main Track","number":102,"pages":{"first":736,"last":742},"keywords":["Computer Vision: Biometrics, Face and Gesture Recognition","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}