Using Grayscale Frequency Statistic to Detect Manipulated Faces in Wavelet-Domain
Gaojian Wang, Wei Li, Qian Jiang, Xin Jin, Xiaohui Cui · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Manipulating facial images results in negative influences on the social association, with deep generative models. Although many detection methods have been proposed, they have either designed sophisticated neural networks that lack enough interpretability, or found defects specific to one manipulation method. To address this issue, we propose a new approach to explore the defects of fake facial images after wavelet transform and call it GFS (Grayscale Frequency Statistics). First, we utilize Haar wavelet transformation to decompose the image into low-frequency approximation, horizontal detail, vertical detail, and diagonal detail. The GFS of real and fake images exhibit different distribution and forms in these four subbands. We qualitatively analyze these differences and quantify them as weights. Then, these four subband images are used to train four CNNs respectively, and the obtained detection results also verify the differences in GFS. After that, we combine the prediction results of the four CNNs and the corresponding weights to further improve the detection performance. We conduct extensive experiments on 11 datasets generated by various facial manipulation methods, and the superior results show the effectiveness of our proposed approach. Our findings indicate that the fake images generated by the current facial manipulation methods cannot simulate real images in wavelet-domain.