Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

【Author】 Ram, R. Saravana; Kumar, M. Vinoth; Al-shami, Tareq M.; Masud, Mehedi; Aljuaid, Hanan; Abouhawwash, Mohamed

【Source】INTELLIGENT AUTOMATION AND SOFT COMPUTING

【影响因子】3.401

【Abstract】Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method. By the proposed deep belief network with loss handling, the manipulation of video/image is found by means of a pairwise learning approach. This proposed approach has improved the accuracy of the detection rate by 98% on various datasets.

【Keywords】Deep fake; deep belief network; fuzzy clustering; feature extraction; pairwise learning

【发表时间】2023

【收录时间】2022-11-24

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-深度学习