article · Neural Computing and Applications
Deepfake technology produces increasingly realistic manipulated videos that complicate the moderation of misinformation across social media platforms. A deep learning framework addresses this challenge through a structured workflow consisting of preprocessing, detection, and prediction. Preprocessing handles video frame extraction, facial identification, alignment, and feature cropping. Dedicated convolutional neural networks analyse specific facial features, notably eyes and noses, whilst a convolutional vision transformer architecture analyses full facial data. The system combines predictions from these three distinct models using a majority voting technique to determine whether content is authentic. Evaluated on the FaceForensics++ and DFDC datasets using metrics such as precision, recall, and F1 score, the individual convolutional neural network configuration achieved 97 per cent accuracy, while the convolutional vision transformer attained 85 per cent accuracy, demonstrating effective automated media forensics.
The spread of manipulated video online makes identifying deceptive media increasingly difficult for digital platforms. Applying specialised neural networks to discrete facial features alongside full face evaluations provides a practical approach to distinguishing authentic media from computer generated alterations, supporting efforts to curb online misinformation.
This research is at an applied and tested stage, validated on standard benchmark datasets rather than deployed software. The technology could be integrated by social media companies, digital platforms, and forensic analysts into content moderation workflows to flag fabricated media. Moving to operational deployment would require testing on real-time video streams and uncurated web uploads.
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Abstract Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection.
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DOI: 10.1007/s00521-024-10181-7
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