A Review of Deep Learning-based Approaches for Deepfake Content Detection

Abstract

Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.

References in corpus (12)

• The Creation and Detection of Deepfakes: A Survey
• Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
• Combining EfficientNet and Vision Transformers for Video Deepfake Detection
• Leveraging Frequency Analysis for Deep Fake Image Recognition
• Fighting Deepfake by Exposing the Convolutional Traces on Images
• Deepfake Video Detection Using Convolutional Vision Transformer
• Fighting deepfakes by detecting GAN DCT anomalies
• Deep Convolutional Pooling Transformer for Deepfake Detection
• Wavelet-Packets for Deepfake Image Analysis and Detection
• Deferred Neural Rendering: Image Synthesis using Neural Textures
• Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
• Forensic Analysis of Synthetically Generated Western Blot Images