computer-vision   1633

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[1901.08971] FaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. At best, this leads to a loss of trust in digital content, but it might even cause further harm by spreading false information and the creation of fake news. In this paper, we examine the realism of state-of-the-art image manipulations, and how difficult it is to detect them - either automatically or by humans. In particular, we focus on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations. We create more than half a million manipulated images respectively for each approach.
facial-recognition  fake-news  machine-learning  computer-vision  research-papers  synthetic-media 
15 days ago by lavallee
[1701.01370] Learning from Synthetic Humans
Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.
computer-vision  synthetic-data  dreaming-about-people  training-data  data-generation  rather-interesting  to-write-about  pose-estimation  image-segmentation 
16 days ago by Vaguery
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | OpenReview
"Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6% top-5 for 32 x 32 px features and Alexnet performance for 16 x16 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies."
deep-learning  convnet  computer-vision  bagnet  imagenet 
18 days ago by arsyed

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