157 citations · 929 across the 89 of their papers we have counts for
13 papers · 1 filter
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…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
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…
ASCAI: Adaptive Sampling for acquiring Compact AI
Mojan Javaheripi, Mohammad Samragh, Tara Javidi +1
This paper introduces ASCAI, a novel adaptive sampling methodology that can learn how to effectively compress Deep Neural Networks (DNNs) for accelerated inference on resource-cons…
A Neural-based Program Decompiler
Cheng Fu, Huili Chen, Haolan Liu +4
Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the…
Decentralized Bayesian Learning over Graphs
Anusha Lalitha, Xinghan Wang, Osman Kilinc +3
We propose a decentralized learning algorithm over a general social network. The algorithm leaves the training data distributed on the mobile devices while utilizing a peer to peer…
Universal Adversarial Perturbations for Speech Recognition Systems
Paarth Neekhara, Shehzeen Hussain, Prakhar Pandey +3
In this work, we demonstrate the existence of universal adversarial audio perturbations that cause mis-transcription of audio signals by automatic speech recognition (ASR) systems.…
SWNet: Small-World Neural Networks and Rapid Convergence
Mojan Javaheripi, Bita Darvish Rouhani, Farinaz Koushanfar
Training large and highly accurate deep learning (DL) models is computationally costly. This cost is in great part due to the excessive number of trained parameters, which are well…
BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks
Huili Chen, Bita Darvish Rouhani, Farinaz Koushanfar
Deep Neural Networks have created a paradigm shift in our ability to comprehend raw data in various important fields ranging from computer vision and natural language processing to…
XONN: XNOR-based Oblivious Deep Neural Network Inference
M. Sadegh Riazi, Mohammad Samragh, Hao Chen +3
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learni…
ARM2GC: Succinct Garbled Processor for Secure Computation
Ebrahim M. Songhori, M. Sadegh Riazi, Siam U. Hussain +2
We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applicatio…
Peer-to-peer Federated Learning on Graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi +1
We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework. The nodes take a Bayesian-like approach via the introductio…
CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs
Mohammad Samragh, Mojan Javaheripi, Farinaz Koushanfar
This paper proposes CodeX, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. CodeX in…