Learning Rich Features for Image Manipulation Detection

Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. We propose a two-stream Faster R-CNN network and train it endto- end to detect the tampered regions given a manipulated image. One of the two streams is an RGB stream whose purpose is to extract features from the RGB image input to find tampering artifacts like strong contrast difference, unnatural tampered boundaries, and so on. The other is a noise stream that leverages the noise features extracted from a steganalysis rich model filter layer to discover the noise inconsistency between authentic and tampered regions. We then fuse features from the two streams through a bilinear pooling layer to further incorporate spatial co-occurrence of these two modalities. Experiments on four standard image manipulation datasets demonstrate that our two-stream framework outperforms each individual stream, and also achieves state-of-the-art performance compared to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read

Strong similarities between the local electronic structure of insulating iron pnictide and lightly doped cuprate

One of the major puzzles regarding unconventional superconductivity is how some of the most interesting superconductors are related to an insulating phase that lies in close proximity. Here we report scanning tunneling microscopy studies of the local electronic structure of Cu doped NaFeAs across the superconductor to insulator transition. We find that in the highly insulating regime the electronic spectrum develops an energy gap with diminishing density of state at the Fermi level. The overall lineshape and strong spatial variations of the spectra are strikingly similar to that of lightly doped cuprates close to the parent Mott insulator. We propose that the suppression of itinerant electron state and strong impurity potential induced by Cu dopants lead to this insulating iron pnictide.Comment: 6 figures, to appear in Phys. Rev.

Future directions in ventilator-induced lung injury associated cognitive impairment: a new sight

Mechanical ventilation is a widely used short-term life support technique, but an accompanying adverse consequence can be pulmonary damage which is called ventilator-induced lung injury (VILI). Mechanical ventilation can potentially affect the central nervous system and lead to long-term cognitive impairment. In recent years, many studies revealed that VILI, as a common lung injury, may be involved in the central pathogenesis of cognitive impairment by inducing hypoxia, inflammation, and changes in neural pathways. In addition, VILI has received attention in affecting the treatment of cognitive impairment and provides new insights into individualized therapy. The combination of lung protective ventilation and drug therapy can overcome the inevitable problems of poor prognosis from a new perspective. In this review, we summarized VILI and non-VILI factors as risk factors for cognitive impairment and concluded the latest mechanisms. Moreover, we retrospectively explored the role of improving VILI in cognitive impairment treatment. This work contributes to a better understanding of the pathogenesis of VILI-induced cognitive impairment and may provide future direction for the treatment and prognosis of cognitive impairment