424 citations · 946 across the 15 of their papers we have counts for
16 papers
OCR Graph Features for Manipulation Detection in Documents
Hailey Joren, Otkrist Gupta, Dan Raviv
Detecting manipulations in digital documents is becoming increasingly important for information verification purposes. Due to the proliferation of image editing software, altering…
MRZ code extraction from visa and passport documents using convolutional neural networks
Yichuan Liu, Hailey James, Otkrist Gupta +1
Detecting and extracting information from Machine-Readable Zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity. However, comput…
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta +1
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensit…
Printing and Scanning Attack for Image Counter Forensics
Hailey Joren, Otkrist Gupta, Dan Raviv
Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image m…
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh +3
In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial pert…
Detailed comparison of communication efficiency of split learning and federated learning
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta +1
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each m…
No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar +2
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still…
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish +1
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method…
Distributed learning of deep neural network over multiple agents
Otkrist Gupta, Ramesh Raskar
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the is…
Maximum-Entropy Fine-Grained Classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar +1
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators t…
Accelerating Neural Architecture Search using Performance Prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar +1
Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations. In this pape…
Pairwise Confusion for Fine-Grained Visual Classification
Abhimanyu Dubey, Otkrist Gupta, Pei Guo +3
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has address…
Designing Neural Network Architectures using Reinforcement Learning
Bowen Baker, Otkrist Gupta, Nikhil Naik +1
At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modi…
Multi-velocity neural networks for gesture recognition in videos
Otkrist Gupta, Dan Raviv, Ramesh Raskar
We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reache…
Deep video gesture recognition using illumination invariants
Otkrist Gupta, Dan Raviv, Ramesh Raskar
In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling. We amalgamate autoencoder and predictor ar…
Reconstruction of hidden 3D shapes using diffuse reflections
Otkrist Gupta, Andreas Velten, Thomas Willwacher +2
We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the…