Abstract: In order to avoid the influence of human factors on skin texture feature extraction of facial images, this paper proposes to detect facial image modification by using convolutional neural network (CNN) algorithm. To the best of our knowledge, this is the first report to use CNN in the detection of human face tampering. Compared with the traditional image classification methods which need complex artificial feature extraction, CNN can learn automatically, acquire features directly from the image, and reduce the difficulty of extracting features in traditional pattern recognition methods, accordingly, gaining a higher recognition rate and wider practicality at the same time. On the basis of the traditional convolutional neural network model, the proposed method builds a new network model for human face tampering detection by adjusting the size of the convolution kernel, reducing the parameters, changing the number of convolutional layer filters, adjusting the alternate mode of the convolutional layer and the pooling layer, and using dropout to improve the generalization ability of the model. Experimental results show that the new network model performs with a high recognition rate and strong robustness in the tamper detection of facial images.

Key words: convolutional neural network (CNN), deep learning, facial image retouching, retouching detection, texture feature