Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection.

ABSTRACT: In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal C

SUBMITTER: Balasubramanian SB 

PROVIDER: S-EPMC9299276 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature