no code implementations • 10 May 2024 • Sushovan Jena, Vishwas Saini, Ujjwal Shaw, Pavitra Jain, Abhay Singh Raihal, Anoushka Banerjee, Sharad Joshi, Ananth Ganesh, Arnav Bhavsar
Briefly, our DCAM module consists of Convolutional Attention blocks distributed across the feature maps of the student network, which essentially learns to masks the irrelevant information during student learning alleviating the "cross-class interference" problem.
no code implementations • 23 Sep 2021 • Sharad Joshi, Yogesh Kumar Gupta, Nitin Khanna
The digital revolution has replaced the use of printed documents with their digital counterparts.
no code implementations • 4 Apr 2020 • Sharad Joshi, Pawel Korus, Nitin Khanna, Nasir Memon
We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e. g., different camera ISP or digital darkroom software).
no code implementations • 27 Mar 2020 • Sharad Joshi, Suraj Saxena, Nitin Khanna
Source printer identification provides essential information about the origin and integrity of a printed document in a fast and cost-effective manner.
no code implementations • 17 Aug 2018 • Sharad Joshi, Suraj Saxena, Nitin Khanna
Knowledge of source smartphone corresponding to a document image can be helpful in a variety of applications including copyright infringement, ownership attribution, leak identification and usage restriction.
1 code implementation • 22 Jun 2017 • Sharad Joshi, Nitin Khanna
This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously.
no code implementations • 20 Jun 2017 • Hardik Jain, Gaurav Gupta, Sharad Joshi, Nitin Khanna
This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document.