Face-Swap Image Recognition Using Diverse Adversarial Noise
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Solution Overview
Problem
Existing face-swapping recognition models suffer from model overfitting due to limited face-swapping data, leading to poor generalization performance and inadequate accuracy and recall rates.
Innovation Solution
The proposed solution involves training a classifier using both fast and slow adversarial noises within a generative adversarial network, enhancing the diversity of adversarial noise to improve recognition accuracy and recall rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional convolutional neural network is pre-trained on natural images and fine-tuned on face-swapping images, then the model can perform face authenticity detection, but the model suffers from overfitting due to limited face-swapping data and has poor generalization performance
Solution Approach 1:
The patent uses a generative adversarial network to generate synthetic face-swapping images that copy and extend the limited real face-swapping data. The generator creates artificial face-swapping images from real images, effectively multiplying the training data quantity while maintaining diversity, thus resolving the overfitting issue caused by limited data
Solution Approach 2:
The patent changes the training approach by introducing adversarial noise with different types (first-type noise from classifier, second-type noise from generator) and adjusting noise injection parameters during training. This transforms the training process to better capture the distribution of face-swapping images, improving generalization performance
2Productivity
If the model is trained with limited face-swapping data, then the training process is faster and requires less computational resources, but the accuracy and recall rate of image recognition are insufficient
Solution Approach 1:
The generative adversarial network copies real face-swapping images to create synthetic training samples, effectively increasing the training data volume without requiring additional real data collection. This maintains training efficiency while significantly improving recognition accuracy through enhanced data diversity
Solution Approach 2:
The patent performs preliminary data augmentation by generating synthetic face-swapping images before the main training process. This preliminary action enriches the training dataset in advance, allowing the model to learn more comprehensive features without extending the actual training time
3Device complexity
If only single-type adversarial noise is used in training, then the training process is simpler, but the diversity of adversarial noise is insufficient leading to inadequate recognition performance
Solution Approach 1:
The patent merges multiple types of adversarial noise (first-type noise obtained from classifier, second-type noise obtained from generator) into a unified training framework. By combining these noise types and integrating them with original images through weighted superposition, the model achieves more comprehensive adversarial training without proportionally increasing complexity
Solution Approach 2:
The training process uses composite noise structures where different noise types are combined with original images using weight parameters. This creates enriched training samples that incorporate multiple noise characteristics, improving the model's ability to handle diverse face-swapping scenarios
Data Source
AI summary
An image recognition method includes: extracting a target image containing a face from a video; determining whether the target image is a forged image through a generative adversarial network, wherein the generative adversarial network comprises a generator and a classifier, and presenting a recognition result indicating whether the target image corresponds to a real image obtained by swapping a face in the real image.