State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

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Authors: Harshika Goyal, Mohammad Saif Wajid, Mohd Anas Wajid, Akib Mohi Ud Din Khanday, Mehdi Neshat, Amir Gandomi

Published: 2025-01-02 03:19:21+00:00

AI Summary

This review paper comprehensively analyzes over 400 publications on deepfake generation and detection techniques, focusing on AI-based learning approaches. It standardizes task definitions and metrics, benchmarks leading approaches, and discusses ethical implications and future research directions.

Abstract

The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methods, such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformers, the outcomes achieved in generating deepfakes have been nothing short of astounding and transformative. Also, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Subsequently, the paper is dedicated to accurately benchmarking leading approaches against prominent datasets, offering thorough assessments of the contributions that have significantly impacted these vital domains. Ultimately, we engage in a thoughtful discussion of the existing challenges, paving the way for continuous advancements in this critical and ever-dynamic study area.

Key findings

Deepfake generation and detection techniques are rapidly evolving. Deep learning-based models, particularly CNNs and hybrid CNN-RNN architectures, show promising results in detection. However, challenges remain in generalizability, adversarial robustness, and ethical considerations.

Approach

The paper conducts a systematic literature review, analyzing existing deepfake generation (GANs, autoencoders, variational encoders, etc.) and detection (CNNs, RNNs, ML, blockchain, statistical methods) techniques. It benchmarks these approaches against prominent datasets and identifies challenges and future research directions.

Datasets

DFDC, FaceForensics++, Celeb-DF, and others; many datasets are mentioned but not all used for benchmarking the main contribution of the paper.

Model(s)

CNNs (including various architectures like ResNet, EfficientNet, InceptionResNetV2), RNNs (LSTMs, GRUs), Transformers, XGBoost, SVM, and various ensemble methods. Many models are mentioned throughout the review but not all are used to evaluate the main contribution of the paper.

Author countries

India, Mexico, United Arab Emirates, Uzbekistan, Australia, Hungary

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