Face Forgery Detection Using Deep Learning Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.55041/ijsrem39643
· OA: W4405395097
The rising occurrence of forged content poses a threat to the authenticity of multimedia. This research suggests a simplified hybrid architecture as an effective method for detecting face forgeries. The framework includes CNN for dependable classification, EfficientNet for robust feature extraction, and MTCNN for precise face detection. MTCNN ensures that high-quality input is produced for feature extraction by accurately localizing facial regions. The CNN classifier utilizes the extracted features in order to distinguish between authentic and manipulated content, and EfficientNet, which has become famous for its good performance and computational efficiency, is able to capture face patterns at a subtle level. Transfer learning enhances the adaptability of the model toward new manipulation techniques because it pre- trains on a large-scale dataset before fine-tuning on data specifically related to deepfakes. Key Words: Deep Learning, Deepfakes, Face Forgery, Multimedia forensics, CNN.
