GAN Face-Swap Recognition Using Dual Adversarial Noise Training
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Solution Overview
Problem
Existing face-swapping recognition models suffer from model overfitting due to limited data, leading to poor generalization performance and low 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 training samples to improve accuracy and recall rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional convolutional neural network is pre-trained on natural images and fine-tuned on face-swapping datasets, then the model can perform basic face-swapping detection, but the model suffers from overfitting due to limited data, leading to poor generalization performance
Solution Approach 1:
The method pre-generates diverse adversarial examples (fast and slow adversarial noises) before training the classifier. By preparing these challenging test cases in advance and incorporating them into the training set, the model is exposed to a broader range of adversarial patterns earlier in the training process, improving its ability to generalize to unseen forged images without requiring extensive additional real forged image data
Solution Approach 2:
The method transforms the training approach by changing the parameters of training samples through adversarial noise generation. By adding fast and slow adversarial noises to real images to create adversarial examples, the training data parameters are modified to include more diverse and challenging cases, which helps the model learn more robust features and improve generalization performance without collecting more real forged images
2Quantity of substance
If the training dataset is limited to available real and forged images, then the model training is feasible with current data, but the accuracy and recall rates are low due to insufficient data diversity
Solution Approach 1:
The method creates synthetic training samples by copying real images and transforming them into adversarial examples through noise generation. Instead of requiring more real forged images, the system generates synthetic adversarial versions of real images (both fast and slow adversarial noises) to expand the training dataset. This copying and transformation approach allows the model to learn from a larger, more diverse set of training examples without needing additional real-world forged image data
Solution Approach 2:
The method enhances training data diversity by changing the parameters of existing real images through adversarial noise addition. By generating fast and slow adversarial noises and combining them with real images, the system creates parameter-modified versions of the original data that exhibit different characteristics and challenges, effectively expanding the training dataset's diversity and improving model performance without increasing the volume of real forged images
Data Source
AI summary
An image recognition method includes: obtaining a to-be-recognized image; determining whether the image is a forged image by recognizing the image through a trained generative adversarial network, the generative adversarial network including a generator and a classifier. Training the classifier includes: obtaining an original image group having a plurality of original images, and a category label of each original image. Each of the plurality of original images includes a real image and a forged image corresponding to the real image. The method includes obtaining using the classifier, for a respective original image of the plurality of original images, first-type noise corresponding to the respective original image; inputting the respective original image into the generator to obtain an output of the generator, and obtaining second-type noise corresponding to the respective original image as the output; and training the classifier using the respective original image, the first-type noise, and the second-type noise.