A Novel Deep Learning Approach for Deepfake Image Detection

Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces face voice with make it realistic-looking. Fake generation unethical threat the community. Nowadays, deepfakes are highly misused cybercrimes for identity theft, cyber extortion, news, financial fraud, celebrity obscenity videos blackmailing, many more. According recent Sensity report, over 96% of obscene content, most victims being from United Kingdom, States, Canada, India, South Korea. In 2019, cybercriminals generated chief executive officer call his organization ask them transfer $243,000 their bank account. crimes rising daily. detection big challenge has high demand digital forensics. An advanced research approach must be built protect blackmailing by detecting content. primary aim our study detect using an efficient framework. A novel predictor (DFP) hybrid VGG16 convolutional neural network architecture proposed this study. dataset real faces building techniques. Xception, NAS-Net, Mobile Net, learning techniques employed comparison. DFP achieved 95% precision 94% accuracy detection. Our outperformed other state-of-the-art studies. helps cybersecurity professionals overcome deepfake-related accurately saving blackmailing.