A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification.

The technology for detecting forged images is good at detecting known forgery methods. It trains neural networks using many original and corresponding forged images created with known methods. However, when encountering unseen forgery methods, the technology performs poorly. Recently, one suggested approach to tackle this problem is to use a hand-crafted generator of forged images to create a range of fake images, which can then be used to train the neural network. However, the aforementioned method has limited detection performance when encountering unseen forging techniques that the hand-craft generator has not accounted for. To overcome the limitations of existing methods, in this paper, we adopt a meta-learning approach to develop a highly adaptive detector for identifying new forging techniques. The proposed method trains a forged image detector using meta-learning techniques, making it possible to fine-tune the detector with only a few new forged samples. The proposed method inputs a small number of the forged images to the detector and enables the detector to adjust its weights based on the statistical features of the input forged images, allowing the detection of forged images with similar characteristics. The proposed method achieves significant improvement in detecting forgery methods, with IoU improvements ranging from 35.4% to 127.2% and AUC improvements ranging from 2.0% to 48.9%, depending on the forgery method. These results show that the proposed method significantly improves detection performance with only a small number of samples and demonstrates better performance compared to current state-of-the-art methods in most scenarios.

Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks.

Water fleas are an important lower invertebrate model that are usually used for ecotoxicity studies. Contrary to mammals, the heart of a water flea has a single chamber, which is relatively big in size and with fast-beating properties. Previous cardiac chamber volume measurement methods are primarily based on ImageJ manual counting at systolic and diastolic phases which suffer from low efficiency, high variation, and tedious operation. This study provides an automated and robust pipeline for cardiac chamber size estimation by a deep learning approach. Image segmentation analysis was performed using U-Net and Mask RCNN convolutional networks on several different species of water fleas such as Moina sp., Daphnia magna, and Daphnia pulex. The results show that Mask RCNN performs better than U-Net at the segmentation of water fleas' heart chamber in every parameter tested. The predictive model generated by Mask RCNN was further analyzed with the Cv2.fitEllipse function in OpenCV to perform a cardiac physiology assessment of Daphnia magna after challenging with the herbicide of Roundup. Significant increase in normalized stroke volume, cardiac output, and the shortening fraction was observed after Roundup exposure which suggests the possibility of heart chamber alteration after roundup exposure. Overall, the predictive Mask RCNN model established in this study provides a convenient and robust approach for cardiac chamber size and cardiac physiology measurement in water fleas for the first time. This innovative tool can offer many benefits to other research using water fleas for ecotoxicity studies.

An /spl omega/-automata approach to the representation of bilevel images.

We use /spl omega/-automata (i.e., automata over infinite words) as a device for representing bilevel images. A major advantage of our approach, as opposed to using the conventional finite automata, lies in that /spl omega/-automata are capable of representing image objects of zero size, such as lines and points. To demonstrate the feasibility of our approach, we also show how a number of image processing operations, including shift, flip, rotation, complement, boundary, difference, union, intersection, and size, can be effectively carried out in the framework of /spl omega/-automata. In particular, the size of an image represented by an /spl omega/-automaton is measured based on the theory of Markov chains. In comparison with other automata-based image representation schemes reported in the literature, our approach is capable of supporting a richer set of operations, which can be performed on the automata directly and easily.