One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.
Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.
Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes.
We also show how the $\ell_2$ norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.
In this work, we develop efficient disruptions of black-box image translation deepfake generation systems.
This type of manipulated images and video have been coined Deepfakes.
no code implementations • 12 Feb 2020 • Nataniel Ruiz, Hao Yu, Danielle A. Allessio, Mona Jalal, Ajjen Joshi, Thomas Murray, John J. Magee, Jacob R. Whitehill, Vitaly Ablavsky, Ivon Arroyo, Beverly P. Woolf, Stan Sclaroff, Margrit Betke
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
Automatic generation of textual video descriptions that are time-aligned with video content is a long-standing goal in computer vision.
This paper addresses the challenging problem of estimating the general visual attention of people in images.
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
Ranked #5 on Head Pose Estimation on AFLW
Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition.