24 citations · 49 across the 5 of their papers we have counts for
6 papers
ReFace: Real-time Adversarial Attacks on Face Recognition Systems
Shehzeen Hussain, Todd Huster, Chris Mesterharm +5
Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an inpu…
Adversarial Scratches: Deployable Attacks to CNN Classifiers
Loris Giulivi, Malhar Jere, Loris Rossi +4
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. These take the form of small perturbations applied to the model's input which le…
A Singular Value Perspective on Model Robustness
Malhar Jere, Maghav Kumar, Farinaz Koushanfar
Convolutional Neural Networks (CNNs) have made significant progress on several computer vision benchmarks, but are fraught with numerous non-human biases such as vulnerability to a…
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
Shehzeen Hussain, Paarth Neekhara, Malhar Jere +2
Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce tru…
Principal Component Properties of Adversarial Samples
Malhar Jere, Sandro Herbig, Christine Lind +1
Deep Neural Networks for image classification have been found to be vulnerable to adversarial samples, which consist of sub-perceptual noise added to a benign image that can easily…
Scratch that! An Evolution-based Adversarial Attack against Neural Networks
Malhar Jere, Loris Rossi, Briland Hitaj +3
We study black-box adversarial attacks for image classifiers in a constrained threat model, where adversaries can only modify a small fraction of pixels in the form of scratches on…