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.

The effects of synthesis time on the plasmonic properties of Ag dendritic nanoforests on Si substrate (Ag-DNF/Si) samples synthesized through the fluoride-assisted galvanic replacement reaction were investigated. The Ag-DNF/Si samples were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, reflection spectroscopy, X-ray diffraction and surface-enhanced Raman spectroscopy (SERS). The prolonged reaction time led to the growth of an Ag-DNF layer and etched Si hole array. SEM images and variations in the fractal dimension index indicated that complex-structure, feather-like leaves became coral-like branches between 30 and 60 min of synthesis. The morphological variation during the growth of the Ag DNFs resulted in different optical responses to light illumination, especially those of light harvest and energy transformation. The sample achieved the most desirable light-to-heat conversion efficiency and SERS response with a 30 min growth time. A longer synthesis time or thicker Ag-DNF layer on the Si substrate did not have superior plasmonic properties.