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

• Published in: PeerJ. Computer science Vol. 8; p. e1040
• Main Authors: Balasubramanian, Saravana Balaji, R, Jagadeesh Kannan, P, Prabu, K, Venkatachalam, Trojovský, Pavel
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 13.07.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms; Analysis; Artificial Intelligence; Artificial neural networks; Big Data; Coders; Computer Vision; Data analysis; Data Mining and Machine Learning; Data Science; Data transfer (computers); Datasets; Deception; Deep fake detection; Deep learning; Deep sparse Auto encoder; Detectors; Digital computers; DNN; Face2Face; Feature extraction; Feature selection; Forensic sciences; Forgery; Image classification; Image manipulation; Information management; Machine learning; Machine vision; Multimedia; Neural networks; Pornography; Privacy, Right of; Social networks; Temporal Convolutional neural network
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1040

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.