Fig. 1: Our algorithm learns to detect and localize image manipulations (splices), despite being trained only on unmanipulated images. The two input images above might look plausible, but our model correctly determined that they have been manipulated because they lack self-consistency: the visual information within the predicted splice region was found to be inconsistent with the rest of the image. IMAGE CREDITS: automatically created splice from Hays and Efros [1] (top), manual splice from Reddit user /u/Name-Albert Einstein (bottom).Abstract. Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent -that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-ofthe-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool. Fig. 2: Anatomy of a splice: One of the most common ways of creative fake images is splicing together content from two different real source images. The insight explored in this paper is that patches from a spliced image are typically produced by different imaging pipelines, as indicated by the EXIF meta-data of the two source images. The problem is that in practice, we never have access to these source images at test time. 1

The tremendous success of ImageNet-trained deep features on a wide range of transfer tasks raises the question: what is it about the ImageNet dataset that makes the learnt features as good as they are? This work provides an empirical investigation into the various facets of this question, such as, looking at the importance of the amount of examples, number of classes, balance between images-per-class and classes, and the role of fine and coarse grained recognition. We pre-train CNN features on various subsets of the ImageNet dataset and evaluate transfer performance on a variety of standard vision tasks. Our overall findings suggest that most changes in the choice of pre-training data long thought to be critical, do not significantly affect transfer performance.

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Optical imaging and stimulation are widely used to study biological events. However, scattering processes limit the depth to which externally focused light can penetrate tissue. Optical fibers and waveguides are commonly inserted into tissue when delivering light deeper than a few millimeters. This approach, however, introduces complications arising from tissue damage. In addition, it makes it difficult to steer light. Here, we demonstrate that ultrasound can be used to define and steer the trajectory of light within scattering media by exploiting local pressure differences created by acoustic waves that result in refractive index contrasts. We show that virtual light pipes can be created deep into the tissue (>18 scattering mean free paths). We demonstrate the application of this technology in confining light through mouse brain tissue. This technology is likely extendable to form arbitrary light patterns within tissue, extending both the reach and the flexibility of light-based methods.