Identification of Fake Faces Using Deep Learning

Abstract

The increasing computational power has signifi- cantly enhanced the capabilities of deep learning algorithms, making it easier to generate hyper-realistic fake facial imagesand videos, commonly known as deepfakes. These manipulated media are often linked to harmful scenarios such as political propaganda,identitytheft,blackmail,andthespreadofmisinfor- mation. Thisworkpresentsanoveldeeplearning-basedapproach foridentifyingAI-generatedfakefaces.Ourmethodcombinesthe strengths ofResNeXtConvolutionalNeuralNetworks(CNNs) to extract frame-level features and Long Short-Term Memory (LSTM)-basedRecurrentNeuralNetworks(RNNs)forsequential temporal analysis, enabling accurate classification of fake versus real faces. To ensure robust performance, the model was trained and evaluated on a large, diverse dataset that includes Face- Forensics++, the Deepfake Detection Challenge, and Celeb-DF. The results demonstrate that our simple yet effective approach achieves high accuracy in detecting fake faces, showcasing its potential for combating the misuse of deepfake technology while paving the way for further advancements in this field.

Introduction

Overview:
The project aims to develop a robust system for detecting deepfake-generated fake faces in images and videos. It combines ResNeXt Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) for temporal analysis, enabling detection of subtle facial manipulations across video frames. A diverse dataset including FaceForensics++, Deepfake Detection Challenge (DFDC), and Celeb-DF ensures generalization against various manipulation techniques. The system is deployed via a user-friendly web application, providing classification results (real or fake) with confidence scores.

Methodology:

1. Data Preprocessing:

   • Split videos into frames.

   • Detect and crop faces using OpenCV.

   • Resize frames to 112×112 pixels.

2. Model Architecture:

   • ResNeXt CNN: Captures spatial inconsistencies in individual frames (e.g., unnatural expressions, blurring).

   • LSTM: Analyzes sequential frame data to detect temporal anomalies (e.g., irregular motion or facial transitions).

3. Training & Optimization:

   • Dataset split: 70% training, 15% validation, 15% testing.

   • Adam optimizer and cross-entropy loss used.

   • Data augmentation (rotations, flips) applied.

4. Evaluation:

   • Metrics: Accuracy, precision, recall, F1-score, confusion matrix.

   • Tested on unseen datasets to ensure robustness.

5. Implementation Tools:

   • PyTorch for model development.

   • OpenCV for video processing.

   • Google Cloud Platform for high-performance training.

   • Django + HTML/CSS for web interface.

Key Features & Parameters:

• Detects deepfake-specific inconsistencies like abnormal blinking, facial expressions, lighting, head pose, skin tone, teeth, and hairstyles.

• Processes videos at ~10 frames per second, balancing speed and accuracy.

• Provides confidence scores and visual output for transparency.

Results:

• High accuracy: 97.76% on FaceForensics++, 93.97% on combined datasets.

• Strong precision, recall, and F1-scores.

• Effective real-world performance on videos from platforms like YouTube.

• User-friendly interface allows non-technical users to upload videos for analysis.

Conclusion

Theprojectfocusesonthedevelopmentandimplementation of a highly effective ”Deepfake Detection System” to address the growing challenge of identifying manipulated media. The systemleveragesadvancedtechnologies,includingdeeplearn- ing frameworks, computer vision, and neural networks, to ac- curately detectfakefacesin videoswithreal-timecapabilities. Its objective is to provide a robust solution for distinguishing between real and fake faces, enabling practical applications in fieldssuchasmediaverification,security,anddigitalforensics. System’sCoreFeatures: 1) PreprocessingPipeline:Thesystemintegratesa comprehensive preprocessing pipeline involving video splitting, face detection, and frame resizing to ensure that only relevant facial features are analyzed during detection. 2) Model Architecture: Combines convolutional neu- ral networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and tem- poral features essential for deepfake detection. 3) Real-Time Detection: Optimized for real-time de- tection, making it suitable for practical applications in live media verification and security contexts.

References

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Copyright

Copyright © 2025 Dr. Vidyarani H J, Mrs. Divya S, Mohammed Sameer, Mohammed Armaan, Puneeth S N, Vishal Gowda T R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.