AI-Generated Face Image Identification with Different Color Space Channel Combinations

With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated face images flood the network, and the forged image is called a deepfake. Those realistic faked images launched a serious challenge to the human eye and the automatic identification system, resulting in many legal, ethical, and social issues. For the needs of network information security, deep-network-generated face image identification based on different color spaces is proposed. Due to the extremely realistic effect of deepfake images, it is difficult to achieve high accuracy with ordinary methods for neural networks, so we used the image processing method here. First, by analyzing the differences in different color space components in the deep learning network model for face sensitivity, a combination of color space components that can effectively improve the discrimination rate of the deep learning network model is given. Second, to further improve the discriminative performance of the model, a channel attention mechanism was added at the shallow level of the model to further focus on the features contributing to the model. The experimental results show that this scheme achieved better accuracy in the same face generation model and in different face generation models than the two compared methods, and its accuracy reached up to 99.10% in the same face generation model. Meanwhile, the accuracy of this model only decreased to 98.71% when coping with a JPEG compression factor of 100, which shows that this model is robust.

Automated summary

With the explosion of network data and the application of deep learning technology, the problem of deepfake images is becoming increasingly serious. Deep-network-generated face image identification based on different color spaces is proposed to enhance the discriminative performance of the model by analyzing the differences in different color spaces and adding a channel attention mechanism in the model.