121 citations · 505 across the 31 of their papers we have counts for
13 papers · 1 filter
Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution
Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti
Diffusion models have transformed image synthesis through iterative denoising, by defining trajectories from noise to coherent data. While their capabilities are widely celebrated,…
Certified Human Trajectory Prediction
Mohammadhossein Bahari, Saeed Saadatnejad, Amirhossein Askari Farsangi +2
Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adver…
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai +1
Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial tra…
PRIME: A few primitives can boost robustness to common corruptions
Apostolos Modas, Rahul Rade, Guillermo Ortiz-Jiménez +2
Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability,…
Vehicle trajectory prediction works, but not everywhere
Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi +4
Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by prov…
Are socially-aware trajectory prediction models really socially-aware?
Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi +3
Our field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigati…
GeoDA: a geometric framework for black-box adversarial attacks
Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard +1
Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challen…
SparseFool: a few pixels make a big difference
Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the…
Divide, Denoise, and Defend against Adversarial Attacks
Seyed-Mohsen Moosavi-Dezfooli, Ashish Shrivastava, Oncel Tuzel
Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustn…
Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance propert…
Classification regions of deep neural networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard +1
The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions cr…
Robustness of classifiers to universal perturbations: a geometric perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +2
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclas…
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +1
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be m…