# A Novel Deep Learning Approach for Deepfake Image Detection
###### Abstract  
Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person's existing media. The deepfake replaces a person's face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Kingdom, United States, Canada, India, and South Korea. In 2019, cybercriminals generated fake audio content of a chief executive officer to call his organization and ask them to transfer $243,000 to their bank account. Deepfake crimes are rising daily. Deepfake media detection is a big challenge and has high demand in digital forensics. An advanced research approach must be built to protect the victims from blackmailing by detecting deepfake content. The primary aim of our research study is to detect deepfake media using an efficient framework. A novel deepfake predictor (DFP) approach based on a hybrid of VGG16 and convolutional neural network architecture is proposed in this study. The deepfake dataset based on real and fake faces is utilized for building neural network techniques. The Xception, NAS-Net, Mobile Net, and VGG16 are the transfer learning techniques employed in comparison. The proposed DFP approach achieved 95% precision and 94% accuracy for deepfake detection. Our novel proposed DFP approach outperformed transfer learning techniques and other state-of-the-art studies. Our novel research approach helps cybersecurity professionals overcome deepfake-related cybercrimes by accurately detecting the deepfake content and saving the deepfake victims from blackmailing.  
artificial intelligence; cybersecurity; cybercrimes; deepfakes; deepfake detection; deep learning; transfer learning +
Footnote †: journal: Medical Image Detection  
0000-0001-9982-2800-3800][PERSON] 00000-0001-9982-2800][PERSON] 00000-0002-0002-3800][PERSON] 0000-0002-0002-3800][PERSON] 00000-0002-0002-3800][PERSON] 0000-0002-3800-3800  
severe crime. Over 96% of the deepfakes are of obscene content, according to a Sensity report [5]. The United Kingdom, United States, Canada, India, and South Korea are the victims of deepfakes. In 2019, cybercriminals scammed a chief executive officer through fake audio content by transferring $243,000 to their bank account [6]. Deepfake-related rising crimes must be controlled and detected with an advanced tool. Face recognition technology is utilized to verify an individual's identity by using a face database [7]. Face recognition is a category of biometric security. Face recognition systems are primarily used in law enforcement and security to control cybercrimes. The two- and three-dimensional pixels-based face images are utilized for face detection. The critical patterns to distinguishing faces are based on the chin, ears, contour of the lips, shape of cheekbones, depth of eye sockets, and distance between eyes. The face recognition technology utilizes deep learning-based networks [8] to identify and learn specific face patterns. The face-related data is converted into a mathematical representation. The deep learning methods are best known for representation learning. Deep neural networks consist of multiple layers of connected neurons. The deep learning methods learned prominent facial biometric patterns [9] for face recognition with high accuracy. The databases containing the faces are utilized for training deep learning-based models. The deep learning-based models outperform the face recognition capabilities of humans. The primary study contributions for deepfake detection are analyzed in detail. Transfer learning and deep learning-based neural network techniques are employed in comparison. The Xception, NAS-Net, Mobile Net, and VGG16 are the employed transfer learning techniques.