Abstract:

[Objective] With the development of computer vision, computer graphics and deep learning technologies, deep face forgery (DeepFake) technology has achieved realistic effects. If used illegally, it will bring serious security risks to individuals, society and the country. Most existing face forgery detection methods infer or predict a specific “fingerprint” of forged faces through one-time training for authenticity detection. When facing new types of forgery, these methods use all data to retrain the network to maintain their detection ability, otherwise their detection effect will drop sharply. However, retraining the network requires a relatively high cost and hinders the model's ability to learn new knowledge in real-time. In view of this, this paper proposes an incremental learning method for detecting forged faces. [Method] The method introduces a dynamic and scalable incremental learning framework to ensure that the model can retain memory of old knowledge while absorbing new knowledge, uses multi-classification to guide binary classification to improve the model's classification ability, and ultimately achieves accurate classification of face images. [Result] Experiments are conducted on two public datasets. On the FF++ expansion set and the ForgeryNet expansion set defined in the experiment, our method can simultaneously maintain the performance of face forgery detection on both old and new tasks. On the ForgeryNet expansion set, existing face forgery detection methods achieves an average accuracy of nearly 98.33%, while our method achieves an average accuracy of 96.16%, while the former uses three times more storage and computing resources than the latter. The last five tasks of the expansion set of ForgeryNet in the experiment are considered as new tasks, with each class containing only 100 training data. Existing methods achieves an average accuracy of near 88.72% on these five tasks, while the method proposed in this paper achieves an average accuracy of 93.83%. [Limitations] To maintain a balance of positive and negative samples, pairs of training samples are required for training, which introduces unnecessary training data and increases the training burden. [Conclusion] The face forgery detection method proposed in this paper improves the effectiveness of the model in detecting constantly emerging forgery samples through incremental learning. Experimental results show that this method can achieve detection capability comparable to existing methods at a lower computational cost; and achieve better forgery detection capability than existing methods in the case of limited training data.

Key words: deepfake detection, deep fakes, incremental learning, continuous learning, catastrophic forgetting