Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
- Title
- Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
- Creator
- Balasubramanian S.B.; Kannan R.J.; Prabu P.; Venkatachalam K.; TrojovskP.
- Description
- 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. 2022. Balasubramanian et al.
- Source
- PeerJ Computer Science, Vol-8
- Date
- 2022-01-01
- Publisher
- PeerJ Inc.
- Subject
- Deep fake detection; Deep learning; Deep sparse auto encoder; Dnn; Face2face; Faceforensics++; Faceswap; Temporal convolutional neural network
- Coverage
- Balasubramanian S.B., Department of Information Technology, Lebanese French University, Erbil, Iraq; Kannan R.J., School of Computer Science and Engineering, VIT Chennai, Tamilnadu, Chennai, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Venkatachalam K., Department of Applied Cybernetics, University of Hradec Krov Hradec Kralove, Czech Republic; TrojovskP., Department of Mathematics, University of Hradec Krov Hradec Kralove, Czech Republic
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 23765992
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Balasubramanian S.B.; Kannan R.J.; Prabu P.; Venkatachalam K.; TrojovskP., “Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 1, 2025, https://archives.christuniversity.in/items/show/15350.