5 citations · 7 across the 3 of their papers we have counts for
7 papers
Enhancing Neural Rendering Methods with Image Augmentations
Juan C. Pérez, Sara Rojas, Jesus Zarzar +1
Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across comput…
3DeformRS: Certifying Spatial Deformations on Point Clouds
Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra +2
3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these model…
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
Juan C. Pérez, Motasem Alfarra, Ali Thabet +2
Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face Recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose…
Generalized Real-World Super-Resolution through Adversarial Robustness
Angela Castillo, María Escobar, Juan C. Pérez +4
Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution i…
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformatio…
Towards Robust General Medical Image Segmentation
Laura Daza, Juan C. Pérez, Pablo Arbeláez
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses ha…