Problem statement
Deepfake videos and images have become a significant threat, contributing to
the spread of misinformation and fake news. As the technology behind deepfakes
continues to advance, traditional methods of detecting manipulated media are becoming
less reliable. This poses a serious challenge to maintaining trust in visual media content
and has far-reaching consequences for society.
Abstract
The proposed deepfake detection algorithm leverages state-of-the-art
techniques in computer vision and machine learning to accurately identify manipulated
videos and images. By combining both traditional image forensics and advanced deep
learning approaches, the algorithm aims to enhance the detection accuracy and
robustness against evolving deepfake techniques.
Outcome
Dataset Collection:
Gather a diverse dataset covering real and deepfake scenarios.
Preprocessing:
Implement preprocessing techniques to enhance the quality of the dataset, including
image normalization, resizing, and data augmentation.
Traditional Image Forensics:
Incorporate classical image forensics techniques to identify inconsistencies in lighting,
shadows, and reflections that are common in deep fake content.
Deep Learning Models:
Develop and train deep neural networks, such as convolutional neural networks (CNNs)
and recurrent neural networks (RNNs), to learn intricate patterns indicative of deepfake
manipulation.
Face and Lip Sync Analysis:
Focus on facial features and lip sync analysis, as deep fakes often struggle to accurately
replicate these elements.
Temporal Analysis:
Integrate temporal analysis to detect anomalies in the continuity and flow of video
sequences, as deep fakes may exhibit unnatural movements over time.
Ensemble Learning:
Employ ensemble learning for a comprehensive and accurate detection approach.
Explainability and Interpretability:
Ensure interpretability with explanations for decision-making.
Continuous Training and Adaptation:
Regularly update the model to adapt to emerging deepfake techniques.
The proposed deepfake detection algorithm aims to significantly enhance the capability
to identify manipulated videos and images. By combining traditional image forensics with
cutting-edge deep learning models, the system provides a robust solution that can be
integrated into media platforms, social networks, and news outlets to combat the spread
of misinformation and fake news. The algorithm’s adaptability and continuous
improvement ensure its effectiveness in addressing the evolving landscape of deepfake
technology.
Reference
Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes more sophisticated and the apps for creating them ever more available, detecting Deepfake has become a challenging task, and accordingly researchers have proposed various deep learning (DL) methods for detection. Though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. The ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. The advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DL-based methods: 99.84% accuracy on FaceForecics++, 99.38% accuracy on DFDC, 99.66% accuracy on VDFD, and 99.43% on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.
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https://ieeexplore.ieee.org/document/9790940/references#references