{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:07:22Z","timestamp":1750306042906,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T00:00:00Z","timestamp":1496707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DARPA","award":["Memex\/XDATA\/D3M programs"],"award-info":[{"award-number":["Memex\/XDATA\/D3M programs"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ICER-1639753, PLR-1348450 and PLR-144562"],"award-info":[{"award-number":["ICER-1639753, PLR-1348450 and PLR-144562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,6,6]]},"DOI":"10.1145\/3078897.3080536","type":"proceedings-article","created":{"date-parts":[[2017,5,25]],"date-time":"2017-05-25T16:27:32Z","timestamp":1495729652000},"page":"16-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["An Approach for Automatic and Large Scale Image Forensics"],"prefix":"10.1145","author":[{"given":"Thamme","family":"Gowda","sequence":"first","affiliation":[{"name":"University of Southern California & Jet Propulsion Laboratory, California Institute of Technology, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyle","family":"Hundman","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, Pasadena, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris A.","family":"Mattmann","sequence":"additional","affiliation":[{"name":"University of Southern California & Jet Propulsion Laboratory, California Institute of Technology, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,6,6]]},"reference":[{"volume-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467","year":"2016","author":"Abadi Mart\u00edn","key":"e_1_3_2_1_1_1"},{"key":"e_1_3_2_1_2_1","unstructured":"Roy Thomas Fielding. 2000. Architectural Styles and the Design of Network-based Software Architectures. Ph.D. Dissertation. AAI9980887.  Roy Thomas Fielding. 2000. Architectural Styles and the Design of Network-based Software Architectures. Ph.D. Dissertation. AAI9980887."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219885"},{"key":"e_1_3_2_1_4_1","unstructured":"Rob Gordon. 1998. Essential JNI: Java Native Interface. Prentice-Hall Inc.   Rob Gordon. 1998. Essential JNI: Java Native Interface. Prentice-Hall Inc."},{"key":"e_1_3_2_1_5_1","unstructured":"Thamme Gowda. 2016. Java CPP: Deserializing tensorflow models for JNI use. https:\/\/github.com\/bytedeco\/javacpp-presets\/issues\/240. (2016). {Online; accessed 20-February-2016}.  Thamme Gowda. 2016. Java CPP: Deserializing tensorflow models for JNI use. https:\/\/github.com\/bytedeco\/javacpp-presets\/issues\/240. (2016). {Online; accessed 20-February-2016}."},{"key":"e_1_3_2_1_6_1","unstructured":"gRPC. 2016. About gRPC. https:\/\/web.archive.org\/web\/20160828193407\/http: \/\/www.grpc.io\/about\/. (2016). {Online; accessed 20-February-2017}.  gRPC. 2016. About gRPC. https:\/\/web.archive.org\/web\/20160828193407\/http: \/\/www.grpc.io\/about\/. (2016). {Online; accessed 20-February-2017}."},{"key":"e_1_3_2_1_7_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classifica- tion with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.   Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classifica- tion with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_3_2_1_8_1","unstructured":"Stanford Vision Lab. 2012. Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) Synsets. http:\/\/image-net.org\/challenges\/LSVRC\/2012\/ browse-synsets. (2012). Accessed: 2017-04-11.  Stanford Vision Lab. 2012. Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) Synsets. http:\/\/image-net.org\/challenges\/LSVRC\/2012\/ browse-synsets. (2012). Accessed: 2017-04-11."},{"key":"e_1_3_2_1_9_1","unstructured":"C Mattmann G Yang H Manjunatha T Gowda A Zhou J Luo and L McGibbney. 2016. Multimedia metadata-based forensics in human trafficking web data. WSDM (2016) 10--13.  C Mattmann G Yang H Manjunatha T Gowda A Zhou J Luo and L McGibbney. 2016. Multimedia metadata-based forensics in human trafficking web data. WSDM (2016) 10--13."},{"key":"e_1_3_2_1_10_1","unstructured":"Chris Mattmann and Jukka Zitting. 2011. Tika in action. Manning Publications Co.   Chris Mattmann and Jukka Zitting. 2011. Tika in action. Manning Publications Co."},{"key":"e_1_3_2_1_11_1","unstructured":"Corianna Jacoby Mentor and Ming Chow. 2016. The Onion Router and the Darkweb. (2016).  Corianna Jacoby Mentor and Ming Chow. 2016. The Onion Router and the Darkweb. (2016)."},{"key":"e_1_3_2_1_12_1","unstructured":"Apache Open NLP. 2016. Apache Open NLP. https:\/\/opennlp.apache.org\/.(2016)  Apache Open NLP. 2016. Apache Open NLP. https:\/\/opennlp.apache.org\/.(2016)"},{"key":"e_1_3_2_1_13_1","unstructured":"MIT NLP. 2016. MITIE: library and tools for information extraction. https: \/\/github.com\/mit-nlp\/MITIE. (2016). {Online; accessed 20-February-2017}.  MIT NLP. 2016. MITIE: library and tools for information extraction. https: \/\/github.com\/mit-nlp\/MITIE. (2016). {Online; accessed 20-February-2017}."},{"key":"e_1_3_2_1_14_1","unstructured":"Federal Business Opportunities. 2016. DARPA-BAA-14-21: Memex. https:\/\/web.archive.org\/web\/20161202033850\/https:\/\/www.fbo.gov\/index's=opportunity&mode=form&id=426485bc9531aaccba1b01ea6d4316ee&tab=core&_cview=0. (2016). {Online; accessed 20-February-2017}.  Federal Business Opportunities. 2016. DARPA-BAA-14-21: Memex. https:\/\/web.archive.org\/web\/20161202033850\/https:\/\/www.fbo.gov\/index's=opportunity&mode=form&id=426485bc9531aaccba1b01ea6d4316ee&tab=core&_cview=0. (2016). {Online; accessed 20-February-2017}."},{"volume-title":"Comparing Web Archives: EOT2008 and EOT2012? What. https:\/\/web.archive.org\/web\/20170220225902\/http:\/\/vphill.com\/journal\/ post\/5962\/. (2014","year":"2014","author":"Phillips Mark E.","key":"e_1_3_2_1_15_1"},{"key":"e_1_3_2_1_16_1","unstructured":"Tasneem Raja. 2016. Semi-Automatic Weapons Without A Background Check Can Be Just A Click Away. (Jun 2016). http: \/\/www.npr.org\/sections\/alltechconsidered\/2016\/06\/17\/482483537\/ semi-automatic-weapons-without-a-background-check-can-be-just-a-click-away  Tasneem Raja. 2016. Semi-Automatic Weapons Without A Background Check Can Be Just A Click Away. (Jun 2016). http: \/\/www.npr.org\/sections\/alltechconsidered\/2016\/06\/17\/482483537\/ semi-automatic-weapons-without-a-background-check-can-be-just-a-click-away"},{"key":"e_1_3_2_1_17_1","unstructured":"Google Research. 2016. Running your models in production with Tensor- Flow Servingt. https:\/\/web.archive.org\/web\/20170201182432\/https:\/\/research. googleblog.com\/2016\/02\/running-your-models-in-production-with.html. (2016).  Google Research. 2016. Running your models in production with Tensor- Flow Servingt. https:\/\/web.archive.org\/web\/20170201182432\/https:\/\/research. googleblog.com\/2016\/02\/running-your-models-in-production-with.html. (2016)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_19_1","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbig- niew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. CoRR abs\/1512.00567 (2015). http:\/\/arxiv.org\/abs\/1512.00567  Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbig- niew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. CoRR abs\/1512.00567 (2015). http:\/\/arxiv.org\/abs\/1512.00567"}],"event":{"name":"ICMR '17: International Conference on Multimedia Retrieval","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Bucharest Romania","acronym":"ICMR '17"},"container-title":["Proceedings of the 2nd International Workshop on Multimedia Forensics and Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3078897.3080536","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3078897.3080536","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3078897.3080536","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:03:09Z","timestamp":1750215789000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3078897.3080536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,6]]},"references-count":19,"alternative-id":["10.1145\/3078897.3080536","10.1145\/3078897"],"URL":"https:\/\/doi.org\/10.1145\/3078897.3080536","relation":{},"subject":[],"published":{"date-parts":[[2017,6,6]]},"assertion":[{"value":"2017-06-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}