@inproceedings{forensics2017,
author = {\textbf{Gowda, Thamme} and Hundman, Kyle and Mattmann, Chris A.},
title = {An Approach for Automatic and Large Scale Image Forensics},
year = {2017},
isbn = {9781450350341},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3078897.3080536},
doi = {10.1145/3078897.3080536},
abstract = {This paper describes the applications of deep learning-based image recognition in the DARPA Memex program and its repository of 1.4 million weapons-related images collected from the Deep web. We develop a fast, efficient, and easily deployable framework for integrating Google's Tensorflow framework with Apache Tika for automatically performing image forensics on the Memex data. Our framework and its integration are evaluated qualitatively and quantitatively and our work suggests that automated, large-scale, and reliable image classification and forensics can be widely used and deployed in bulk analysis for answering domain-specific questions.},
booktitle = {Proceedings of the 2nd International Workshop on Multimedia Forensics and Security},
pages = {16–20},
numpages = {5},
keywords = {information retrieval, image recognition, multimedia forensics},
location = {Bucharest, Romania},
series = {MFSec '17},
}

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