The present findings suggest that Danshen can be used in the treatment of xerostomia, to avoid the systemic side effects associated with muscarinic drugs.
Source Camera Identification (SCI) has been playing an important role in the security field for decades. With the development of Deep Learning, the performance of SCI has been noteworthily improved. However, most of the proposed methods are forensic only for a single camera identification category, e.g., the camera model identification. For exploiting the coupling between different camera categories, we present a new coding method. That is, we apply the multi-task training method to regress the categories, namely, to classify brands, models and devices synchronously in a single network. Different from the common multi-task method, we obtain the multi-class classification result by just one single label classification. To be specific, we classify the categories in a progressive way that the parent category classification result will be used in the child category classification (a detailed explanation will be given later in the main context). Also, by appropriately increasing the redundancy of the coding method for classifying new camera categories, the training time can be greatly reduced. To better extract camera attributes, we propose an adaptive filter. Additionally, we propose an auxiliary classifier that only focuses on the camera model re-classification, due to the low performance of the main classifier on certain models. Lastly, the extensive experiments show that our methods have a better performance than other existing methods. INDEX TERMS Source camera identification, deep learning, multi-task training, camera categories coupling coding, adaptive filter, auxiliary classifier.
As deepfake becomes more sophisticated, the demand for fake facial image detection is increasing. Although great progress has been made in deepfake detection, the performance of most existing deepfake detection methods degrade significantly when these methods are applied to detect low-quality images for the disappearance of key clues during the compression process. In this work, we mine frequency domain and RGB domain information to specifically improve the detection of low-quality compressed deepfake images. Our method consists of two modules: (1) a preprocessing module and (2) a classification module. In the preprocessing module, we utilize the Haar wavelet transform and residual calculation to obtain the mid-high frequency joint information and fuse the frequency map with the RGB input. In the classification module, the image obtained by concatenation is fed to the convolutional neural network for classification. Because of the combination of RGB and frequency domain, the robustness of the model has been greatly improved. Our extensive experimental results demonstrate that our approach can not only achieve excellent performance when detecting low-quality compressed deepfake images, but also maintain great performance with high-quality images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.