Visual Realism Assessment for Face-swap Videos

Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step towards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional hand-crafted features to different kinds of deep-learning features. The evaluations are based on a recent competition dataset named DFGC 2022, which contains 1400 diverse face-swap videos that are annotated with Mean Opinion Scores (MOS) on visual realism. Comprehensive experiment results using 11 models and 3 protocols are shown and discussed. We demonstrate the feasibility of devising effective VRA models for assessing face-swap videos and methods. The particular usefulness of existing deepfake detection features for VRA is also noted. The code can be found at https://github.com/XianyunSun/VRA.git.Comment: Accepted by ICIG 202

Aldose reductase regulates microglia/macrophages polarization through the cAMP response element-binding protein after spinal cord injury in mice.

Inflammatory reactions are the most critical pathological processes occurring after spinal cord injury (SCI). Activated microglia/macrophages have either detrimental or beneficial effects on neural regeneration based on their functional polarized M1/M2 subsets. However, the mechanism of microglia/macrophage polarization to M1/M2 at the injured spinal cord environment remains unknown. In this study, wild-type (WT) or aldose reductase (AR)-knockout (KO) mice were subjected to SCI by a spinal crush injury model. The expression pattern of AR, behavior tests for locomotor activity, and lesion size were assessed at between 4 h and 28 days after SCI. We found that the expression of AR is upregulated in microglia/macrophages after SCI in WT mice. In AR KO mice, SCI led to smaller injury lesion areas compared to WT. AR deficiency-induced microglia/macrophages induce the M2 rather than the M1 response and promote locomotion recovery after SCI in mice. In the in vitro experiments, microglia cell lines (N9 or BV2) were treated with the AR inhibitor (ARI) fidarestat. AR inhibition caused 4-hydroxynonenal (HNE) accumulation, which induced the phosphorylation of the cAMP response element-binding protein (CREB) to promote Arg1 expression. KG501, the specific inhibitor of phosphorylated CREB, could cancel the upregulation of Arg1 by ARI or HNE stimulation. Our results suggest that AR works as a switch which can regulate microglia by polarizing cells to either the M1 or the M2 phenotype under M1 stimulation based on its states of activity. We suggest that inhibiting AR may be a promising therapeutic method for SCI in the future

A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition

Object-based change detection for vegetation disturbance and recovery using Landsat time series

Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, “object-based change detection of trends in disturbance and recovery” (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000–2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user’s accuracy and removed salt-and-pepper noise. Spatial–temporal maps of vegetation change showed that the recovery area was 571.27 km2 while the disturbed area was 297.65 km2, accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time