An Improved Self-Training Model with Fine-Tuning Teacher/Student Exchange Strategy to Detect Computer-Generated Images

Abstract

Computer-generated (CG) images have become indistinguishable from natural images due to powerful image rendering technology. Fake CG images have brought huge troubles to news media, judicial forensics, and other fields. How to detect CG image has become a key point to solve the problems mentioned above. The image classification method based on deep learning, due to its strong self-learning ability, can automatically determine the differences in the image features between CG images and natural images and can be used to detect CG images. However, deep learning often requires a large amount of labeled data, which is usually a tedious and complex task. This paper proposes an improved self-training strategy with fine-tuning teacher/student exchange (FTTSE) to solve the problem of missing labeled datasets. Our method is actually a strategy based on semisupervised learning to train the teacher model through labeled data and to predict the unlabeled data by the teacher model to generate pseudo labels. The student model is obtained by continuous training on the mixed dataset composed of labeled and pseudo-labeled data. A teacher/student exchange strategy is designed for iterative training; i.e., the identities of the teacher model and the student model are exchanged at the beginning of each round of iteration. And then the new teacher model is used to predict pseudo labels, and the new student model exchanged from teacher model in the previous round of iteration is fine-tuned and retrained by the mixed dataset with new pseudo labels. Furthermore, we introduced malicious image attacks to perturb the mixed dataset to improve the robustness of the student model. The experimental results show that the improved self-training model we proposed can stably maintain the image classification ability even if the testing images are maliciously attacked. After iterative training, the CG image detection accuracy of the final model increases by 5.18%. The robustness against 100% malicious attacks is also improved, where the final trained model has an accuracy improvement of 7.63% higher than the initial model. The self-training model with FTTSE strategy proposed in this paper can effectively enhance the detection ability of the existing model and can greatly improve the robustness of the model with iterative training.