Work

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

Year: 2022

Type: article

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 CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.1040

Authors: Saravana Balaji Balasubramanian, Jagadeesh Kannan R, Prabu P, Venkatachalam K, Pavel Trojovský

Institutions: Lebanese French University, Vellore Institute of Technology University, Christ University, University of Hradec Králové

Language: English

Topic: Digital Media Forensic Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,395

Funder: Univerzita Hradec Králové