Nataniel Ruiz

I am a fourth year PhD candidate at Boston University in the Image & Video Computing group, where I obtained the Dean's Fellowship. I am advised by Professor and Dean of the College of Arts and Sciences Stan Sclaroff. My primary research focus is computer vision and machine learning.

I am currently interning at Amazon working with Javier Romero, Timo Bolkart, Ming C. Lin and Raja Bala. I interned at Apple AI Research during the 2019 and 2020 Summers where I worked with Dr. Barry-John Theobald and Dr. Nicholas Apostoloff. In 2018 I was a Spring/Summer intern at the NEC-Labs Media Analytics Department, where I worked with Prof. Manmohan Chandraker and Dr. Samuel Schulter. I graduated from Georgia Tech in Fall 2017 with a M.Sc. in Computer Science specializing in Machine Learning, advised by Prof. James Rehg at the Center for Behavioral Imaging.

Recently, I have been selected as a Twitch Research Fellowship finalist for the year 2020 and was a second round interviewee for the Open Phil AI Fellowship. I also appeared on the popular Machine Learning and AI podcast TWIML AI talking about my recent work on defending against deepfakes. While on a 5-year valedictorian scholarship, I obtained my B.Sc. and M.Sc. from Ecole Polytechnique in Paris, France. Aditionally, I worked as an intern at MIT CSAIL with Dr. Kalyan Veeramachaneni and Dr. Lalana Kagal.

nruiz9 [at]  |  CV  |  Google Scholar  |  GitHub  |  LinkedIn


Currently, my main interests include generative models, image translation, adversarial attacks, facial analysis, simulation and behavior understanding. My current goal is to develop methods to protect one's image and likeness by developing effective adversarial attacks against deepfake generation systems. My larger goal is to understand how deep neural networks work, learn and generalize. I believe the intersection of adversarial attacks and generative models will be an important component in this endeavor. I also enjoy building interesting and visually appealing demos of my research work and sharing them.

Simulated Adversarial Testing of Face Recognition Models
N. Ruiz, A. Kortylewski, W. Qiu, Cihang Xie, Sarah Adel Bargal, Alan Yuille, S. Sclaroff
Under Review for Conference, 2021

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. We apply this method in a face recognition scenario. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected components in the latent space of the simulator. We present a method to find these adversarial regions.

Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Benjamin Spetter-Goldstein, N. Ruiz, Sarah Adel Bargal
ICML Adversarial Machine Learning Workshop, 2021

Through examining and measuring both the effectiveness of recent popular black-box attacks in the face recognition setting and their corresponding human perceptibility through survey data, we demonstrate the trade-offs in perceptibility that occur as attacks become more aggressive. We also show how the norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.

MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias
N. Ruiz, B. J. Theobald, A. Ranjan, A. H. Abdelaziz, N. Apostoloff
Under Review for Conference, 2021

We present MorphGAN, a powerful GAN that can control the head pose and facial expression of a face image. MorphGAN can generalize to unseen identities (one-shot) and generates realistic outputs that conserve the input identity. We use this simulator to generate new images to test the robustness of a facial recognition deep network with respect to pose and expression, without the need to collect new test data. Aditionally, we show that the generated images can be used to augment small datasets of faces with new poses and expressions to improve recognition accuracy.

Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks
N. Ruiz, S. Bargal, S. Sclaroff
Under Review for Conference, 2021

We are the first to attack image translation systems in a black-box scenario. Our novel algorithm "learning universal perturbations" (LUP) significantly reduces the number of queries needed for black-box attacks by leaking and exploiting information from the deep network. Our attacks can be used to protect images from manipulation and to prevent deepfake generation in the real world.

Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
N. Ruiz, S. Bargal, S. Sclaroff
CVPR Workshop on Adversarial Machine Learning in Computer Vision and ECCV Workshop on Advances in Image Manipulation, 2020
podcast  /  code  /  video demo

We present a method for disrupting the generation of deepfakes by generating adversarial attacks for image translation networks. We present the first instance of attacks against conditional image translation networks. Our attacks transfer across different conditioning classes. We also present the first instance of adversarial training for generative adversarial networks as a first step towards robust image translation networks.

Detecting Attended Visual Targets in Video
E. Chong, Y. Wang, N. Ruiz, J.M. Rehg
Conference on Computer Vision and Pattern Recognition (CVPR), 2020

We present the most advanced video attention detection method to-date. By leveraging our new large video dataset of gaze behavior and a new neural network architecture we achieve state-of-the-art performance on three gaze following datasets and compelling real-world performance.

Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
N. Ruiz, M. Jalal, V. Ablavsky, D. Allessio, J. Magee, J. Whitehill, I. Arroyo, B. Woolf, S. Sclaroff, M. Betke
IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2021

In order to improve behavior prediction and behavior understanding of students using an Intelligent Tutoring System, we present a novel instance of affect transfer learning that leverages a large affect recognition dataset.

Learning To Simulate
N. Ruiz, S. Schulter, M. Chandraker
International Conference on Learning Representations (ICLR), 2019

We propose an algorithm that automatically learns parameters of a simulation engine to generate training data for a machine learning model in order to maximize performance. We present experiments on a toy example, an object counting vision task and on semantic segmentation for traffic scenes both on simulated and real evaluation data.

Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
E. Chong, N. Ruiz, Y. Wang, Y. Zhang, A. Rozga, J.M. Rehg
European Conference on Computer Vision (ECCV), 2018
poster  /  bibtex

We are the first to tackle the generalized visual attention prediction problem, which consists in predicting the 3D gaze vector, attention heatmaps inside of the image frame and whether the subject is looking inside or outside of the image. To this end, we jointly model gaze and scene saliency using a neural network architecture trained on three heterogeneous datasets.

Fine-Grained Head Pose Estimation Without Keypoints
N. Ruiz, E. Chong, J.M. Rehg
Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2018   (Oral Presentation)
code  /  video demo  /  poster  /  bibtex

By using a deep network trained with a binned pose classification loss and a pose regression loss on a large dataset we obtain state-of-the-art head pose estimation results on several popular benchmarks. Our head pose estimation models generalize to different domains and work on low-resolution images. We release an open-source software package with pre-trained models that can be used directly on images and video.

Learning to Localize and Align Fine­-Grained Actions to Sparse Instructions
M. Hahn, N. Ruiz, J.B. Alayrac, I. Laptev, J.M. Rehg
arXiv Preprint, 2018

We present a framework that, given an instructional video, can localize atomic action segments and align them to the appropriate instructional step using object recognition and natural language.

Detecting Gaze Towards Eyes in Natural Social Interactions and Its Use in Child Assessment
E. Chong, K. Chanda, Z. Ye, A. Southerland, N. Ruiz, R.M. Jones, A. Rozga, J.M. Rehg
UbiComp and Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017
(Oral Presentation and Distinguished Paper Award - 3% award rate)

We introduce the Pose-Implicit CNN, a novel deep learning architecture that predicts eye contact while implicitly estimating the head pose. The model is trained on a dataset comprising 22 hours of 156 play session videos from over 100 children, half of whom are diagnosed with Autism Spectrum Disorder.

Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
N. Ruiz, J.M. Rehg
arXiv Preprint, 2017
code  /  bibtex

In order to help the wider scientific community, we release a pre-trained deep learning face detector that is easy to download and use on images and video.


N. Ruiz
video demo  /  app apk

Real-time object detection on Android using the YOLO network with TensorFlow.