With the rapid growth of generative adversarial networks (GANs), the photo-realistic image can be easily generated from a low-dimension random vector nowadays. The generated image, however, can be used to synthesize some persons who may have a potent effect on society with radical contents. Since there are many ways to produce the photo-realistic face image based on different GANs, it is hard to collect training images of all possible generative models so that the learning-based approach may be unable to effectively detect the fake image generated by an excluded generative model. To overcome this shortcoming, we propose a two-step pairwise learning approach to learn the common fake feature over the training images generated by several generative models. First, we use pairwise learning o learn the common fake features of the generated images by several GANs for judging whether the picture is real or fake. Furthermore, we also propose a novel coupled network to adequately capture the local and global image features for fake or real images. Experimental results demonstrate that the proposed method outperforms the baseline supervised learning methods for fake face image detection.
Since there are many GANs, it is hard to collect all training images from these GANs. Instead of learning the fake features for each GAN, we tend to learn the common fake features from the collected training images.
*Use contrastive loss or triplet loss to learn the common features from the generated images synthesized by several GANs !!
First, we learn the common fake feature via the proposed pairwise learning.
Second, we adopt a small neural network as the classifier so that we can update both classifier and the common fake features.
We adopt a two-stream network architecture consisting of the CNN with 3x3 and 5x5 kernels to capture the fake features locally and globally.
用一個雙路架構的CNN網路 (分別由3x3 and 5x5的Convolutional kernels組合而成) 來學習較局部性與全域性的偽造特徵。
To verify the generalization of the proposed method, we remove the training images generated by one of the GANs as the training set. For example, if we remove the training image generated by PAGAN from the training set, then the trained fake face detector will not learn the fake feature from PGGAN. Afterward, the learned fake face detector is used to detect the test set consisting of the real images and fake images generated by PAGAN. Table I shows the performance comparison between the proposed fake face detector, other baseline methods, and methods in  in terms of accuracy, precision, and recall. The proposed method significantly outperforms other state-of-the-art methods due to the common fake features can be well captured by our CFF and CDNN architecture. It is also verified that the proposed method is more generalized and effective than others.
訓練過程中，從收集的六個GAN中去除其中一個GAN，並將訓練好的模型拿來測試該GAN藉此檢驗本方法是否可以推廣到未來其他的GAN。實驗結果證明本方法可以得到最佳的效果 (Precision / Recall rate)
Moreover, the proposed model can be used to visualize the fake regions of the generated image by extracting the last convolutional layer and mapping the responses to the image domain. The visualized feature map for fake regions localization. (a) and (c) are the generated face images by PGGAN  and DCCAN  respectively. (b) and (d) are the localized fake regions for the fake faces of (a) and (b).
除了可以偵測影像本身是偽造還是真實影像之外，亦可透過Feature visualization engineering的方式來獲得人臉偽造區域資訊。
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