activity
20172020
most citedCountering Adversarial Images using Input Transformations

437 citations · 624 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG202012 cited

Fairness with Overlapping Groups

Forest Yang, Moustapha Cisse, Sanmi Koyejo

In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this sta…

cs.LG2018

Fooling End-to-end Speaker Verification by Adversarial Examples

Felix Kreuk, Yossi Adi, Moustapha Cisse +1

Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems us…

cs.LG2018

Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring

Yossi Adi, Carsten Baum, Moustapha Cisse +2

Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expen…

cs.LG201711 cited

Unbounded cache model for online language modeling with open vocabulary

Edouard Grave, Moustapha Cisse, Armand Joulin

Recently, continuous cache models were proposed as extensions to recurrent neural network language models, to adapt their predictions to local changes in the data distribution. The…

cs.CV2017437 cited

Countering Adversarial Images using Input Transformations

Chuan Guo, Mayank Rana, Moustapha Cisse +1

This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Spe…

stat.ML2017164 cited

Houdini: Fooling Deep Structured Prediction Models

Moustapha Cisse, Yossi Adi, Natalia Neverova +1

Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and…