Abstract
Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
Highlights
Deep learning-based generative models, such as variational autoencoders and generative adversarial networks (GANs), have been widely used to synthesize the photo-realistic partial or whole content of an image or a video
To meet the current requirement for the GANs-based generator of fake image detection, we propose a modified network structure, including a pairwise learning approach, called the common fake feature network (CFFN)
deep fake detector (DeepFD), the fake images generated by the progressive growth of GANs (PGGAN) and the corresponding real images were used to evaluate the performance of the trained fake face detector
Summary
Deep learning-based generative models, such as variational autoencoders and generative adversarial networks (GANs), have been widely used to synthesize the photo-realistic partial or whole content of an image or a video. Since deep neural networks have been widely used in various recognition tasks, we can adopt a deep neural network to detect fake images generated by the GANs. Recently, the deep learning-based approached for fake image detection using supervised learning has been studied. To meet the current requirement for the GANs-based generator of fake image detection, we propose a modified network structure, including a pairwise learning approach, called the common fake feature network (CFFN).
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