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Learning to mask: Towards generalized face forgery detection
Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types.
General GAN-generated image detection by data augmentation in fingerprint domain
To our knowledge, we are the first to conduct data augmentation in the fingerprint domain.
Learning Second Order Local Anomaly for General Face Forgery Detection
We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies.
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