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.

A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning

“…The term "deepfake" was coined in 2017 by a Reddit user named deepfake, referring to synthetic media generated using AI-driven adversarial networks [55]. Deepfakes utilize video, audio, and face-swapping technologies.…”

Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

“…Feature-based methods focus on extracting distinctive features from video frames to identify anomalies indicative of deepfake content. [15] proposed a deepfake predictor (DFP) approach leveraging a hybrid of VGG16 and convolutional neural network architecture. Their method, trained on a deepfake dataset comprising real and fake faces, achieved impressive precision and accuracy rates for deepfake detection.…”

A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and data fusion

“…In addition to text information, images are essential in fake news detection since they greatly impact the way news is spread [ 39 ]. The research presented by Raza, Munir [ 57 ] focuses on image-based counterfeit detection models. A method for detecting deep fake content called a deepfake predictor (DFP) was proposed in this study and was built on the VGG-16 and CNN architectures.…”