{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T07:24:27Z","timestamp":1781335467731,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":93,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2429835"],"award-info":[{"award-number":["2429835"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"DOD U.S. Department of Defense","doi-asserted-by":"publisher","award":["H98230-19-D-0012"],"award-info":[{"award-number":["H98230-19-D-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,26]]},"DOI":"10.1145\/3706598.3713711","type":"proceedings-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T14:48:11Z","timestamp":1745851691000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Understanding and Empowering Intelligence Analysts: User-Centered Design for Deepfake Detection Tools"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2987-4884","authenticated-orcid":false,"given":"Y. Kelly","family":"Wu","sequence":"first","affiliation":[{"name":"ESL Global Cybersecurity Institute, Rochester Institute of Technology, Rochester, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4707-7035","authenticated-orcid":false,"given":"Saniat Javid","family":"Sohrawardi","sequence":"additional","affiliation":[{"name":"ESL Global Cybersecurity Institute, Rochester Institute of Technology, Rochester, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6418-8002","authenticated-orcid":false,"given":"Candice R.","family":"Gerstner","sequence":"additional","affiliation":[{"name":"Research Directorate and AI Security Center, National Security Agency, Fort George G. Meade, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8489-6347","authenticated-orcid":false,"given":"Matthew","family":"Wright","sequence":"additional","affiliation":[{"name":"ESL Global Cybersecurity Institute, Rochester Institute of Technology, Rochester, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_3_3_2_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS 2018)","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity Checks for Saliency Maps. In Advances in Neural Information Processing Systems (NeurIPS 2018) , Vol.\u00a031. Curran Associates, Inc., Montr\u00e9al, Canada."},{"key":"e_1_3_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00109"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00338"},{"key":"e_1_3_3_3_6_2","first-page":"38","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","author":"Agarwal Shruti","year":"2019","unstructured":"Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li. 2019. Protecting World Leaders Against Deep Fakes. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE, Long Beach, CA, USA, 38\u201345."},{"key":"e_1_3_3_3_7_2","unstructured":"National\u00a0Security Agency. n. d.. Career Fields. https:\/\/www.intelligencecareers.gov\/nsa\/career-fields#intelligence-analysis. Accessed: 2024-11-13."},{"key":"e_1_3_3_3_8_2","unstructured":"Sensity AI. 2024. Sensity | Deepfakes detection. https:\/\/sensity.ai\/deepfake-detection. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_9_2","doi-asserted-by":"publisher","unstructured":"Gajanan\u00a0K. Birajdar and Vijay\u00a0H. Mankar. 2013. Digital image forgery detection using passive techniques: A survey. Digit. Investig. 10 3 (Oct. 2013) 226\u2013245. 10.1016\/j.diin.2013.04.007","DOI":"10.1016\/j.diin.2013.04.007"},{"key":"e_1_3_3_3_10_2","doi-asserted-by":"crossref","unstructured":"Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3 2 (2006) 77\u2013101.","DOI":"10.1191\/1478088706qp063oa"},{"key":"e_1_3_3_3_11_2","unstructured":"C2PA. 2024. Content Credentials. https:\/\/www.contentcredentials.org\/."},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCB48548.2020.9304936"},{"key":"e_1_3_3_3_13_2","doi-asserted-by":"publisher","unstructured":"A. Chintha B. Thai S.\u00a0J. Sohrawardi K.\u00a0M. Bhatt A. Hickerson M. Wright and R. Ptucha. 2020. Recurrent Convolutional Structures for Audio Spoof and Video Deepfake Detection. IEEE Journal of Selected Topics in Signal Processing 14 5 (2020) 1024\u20131037. 10.1109\/JSTSP.2020.2999185","DOI":"10.1109\/JSTSP.2020.2999185"},{"key":"e_1_3_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00439"},{"key":"e_1_3_3_3_15_2","doi-asserted-by":"crossref","unstructured":"Matteo Cristani and Roberta Cuel. 2005. A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems (IJSWIS) 1 2 (2005) 49\u201369.","DOI":"10.4018\/jswis.2005040103"},{"key":"e_1_3_3_3_16_2","unstructured":"DARPA. 2016. Media Forensics (MediFor). https:\/\/www.darpa.mil\/program\/media-forensics."},{"key":"e_1_3_3_3_17_2","unstructured":"DARPA. 2021. Semantic Forensics (SemaFor). https:\/\/www.darpa.mil\/program\/semantic-forensics."},{"key":"e_1_3_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICME57554.2024.10687902"},{"key":"e_1_3_3_3_19_2","unstructured":"deepware.ai. 2025. Deepware | Scan & Detect Deepfake videos. https:\/\/deepware.ai. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_20_2","unstructured":"Reality Defender. 2024. Enterprise-Grade Deepfake Detection. https:\/\/realitydefender.com. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","unstructured":"Stephen\u00a0L. Dorton and Samantha\u00a0B. Harper. 2022. A Naturalistic Investigation of Trust AI and Intelligence Work. Journal of Cognitive Engineering and Decision Making 16 4 (2022) 222\u2013236. 10.1177\/15553434221103718","DOI":"10.1177\/15553434221103718"},{"key":"e_1_3_3_3_22_2","unstructured":"DuckDuckGoose. 2024. DuckDuckGoose. https:\/\/www.duckduckgoose.ai. Accessed: 2024-11-13."},{"key":"e_1_3_3_3_23_2","doi-asserted-by":"publisher","unstructured":"Walid El-Shafai Mona\u00a0A. Fouda El-Sayed\u00a0M. El-Rabaie and Nariman\u00a0Abd El-Salam. 2024. A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends. Multimedia Tools and Applications 83 2 (Jan. 2024) 4241\u20134307. 10.1007\/s11042-023-15609-1","DOI":"10.1007\/s11042-023-15609-1"},{"key":"e_1_3_3_3_24_2","unstructured":"Ctrl\u00a0Shift Face. 2019. The Dark Knight\u2019s Tale [DeepFake]. https:\/\/www.youtube.com\/watch?v=TgcvQA6-qBg&ab_channel=CtrlShiftFace."},{"key":"e_1_3_3_3_25_2","unstructured":"Federation of American scientists. n. d.. The Intelligence Cycle. https:\/\/irp.fas.org\/cia\/product\/facttell\/intcycle.htm. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_26_2","doi-asserted-by":"publisher","unstructured":"William\u00a0D. Ferreira Cristiane\u00a0B.R. Ferreira Gelson da Cruz J\u00fanior and Fabrizzio Soares. 2020. A review of digital image forensics. Computers & Electrical Engineering 85 (2020). 10.1016\/j.compeleceng.2020.106685","DOI":"10.1016\/j.compeleceng.2020.106685"},{"key":"e_1_3_3_3_27_2","doi-asserted-by":"publisher","unstructured":"Jessica\u00a0L. Feuston and Jed\u00a0R. Brubaker. 2021. Putting Tools in Their Place: The Role of Time and Perspective in Human-AI Collaboration for Qualitative Analysis. Proc. ACM Hum.-Comput. Interact. 5 CSCW2 Article 469 (Oct. 2021) 25\u00a0pages. 10.1145\/3479856","DOI":"10.1145\/3479856"},{"key":"e_1_3_3_3_28_2","unstructured":"Laboratory for Analytic\u00a0Sciences. n. d.. A Day in the Life of an NSA Intelligence Analyst. https:\/\/tae.ncsu-las.net\/documents. Accessed: 2024-11-13."},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00015"},{"key":"e_1_3_3_3_30_2","doi-asserted-by":"publisher","unstructured":"Marzyeh Ghassemi Luke Oakden-Rayner and Andrew\u00a0L Beam. 2021. The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health 3 11 (Nov. 2021). 10.1016\/S2589-7500(21)00208-9","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"e_1_3_3_3_31_2","unstructured":"Dennis\u00a0J Gleeson. 2023. Artificial Intelligence for Analysis: The Road Ahead. Studies in Intelligence 67 4 (2023) 11\u201315."},{"key":"e_1_3_3_3_32_2","doi-asserted-by":"crossref","unstructured":"Michael Gruninger Olivier Bodenreider Frank Olken Leo Obrst and Peter Yim. 2008. Ontology Summit 2007 - Ontology taxonomy folksonomy: Understanding the distinctions. Appl. Ontol. 3 3 (Aug. 2008) 191\u2013200.","DOI":"10.3233\/AO-2008-0052"},{"key":"e_1_3_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10094720"},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"e_1_3_3_3_35_2","doi-asserted-by":"publisher","unstructured":"Bing Han Xiaoguang Han Hua Zhang Jingzhi Li and Xiaochun Cao. 2021. Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM. IEEE Transactions on Biometrics Behavior and Identity Science 3 3 (2021) 320\u2013331. 10.1109\/TBIOM.2021.3065735","DOI":"10.1109\/TBIOM.2021.3065735"},{"key":"e_1_3_3_3_36_2","unstructured":"Silvan Heller Luca Rossetto and Heiko Schuldt. 2018. The PS-Battles Dataset - an Image Collection for Image Manipulation Detection. arxiv:https:\/\/arXiv.org\/abs\/1804.04866\u00a0[cs.MM]"},{"key":"e_1_3_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00339"},{"key":"e_1_3_3_3_38_2","volume-title":"Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes.","author":"Janis Irving\u00a0L","year":"1972","unstructured":"Irving\u00a0L Janis. 1972. Victims of groupthink: A psychological study of foreign-policy decisions and fiascoes.Houghton Mifflin, Boston, MA."},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","unstructured":"Abdul\u00a0Rehman Javed Zunera Jalil Wisha Zehra Thippa\u00a0Reddy Gadekallu Doug\u00a0Young Suh and Md.\u00a0Jalil Piran. 2021. A comprehensive survey on digital video forensics: Taxonomy challenges and future directions. Eng. Appl. Artif. Intell. 106 C (Nov. 2021). 10.1016\/j.engappai.2021.104456","DOI":"10.1016\/j.engappai.2021.104456"},{"key":"e_1_3_3_3_40_2","doi-asserted-by":"publisher","unstructured":"Nickson Karie and Victor Kebande. 2016. Building Ontologies for Digital Forensic Terminologies. International Journal of Cyber-Security and Digital Forensics 5 (04 2016) 75\u201382. 10.17781\/P002032","DOI":"10.17781\/P002032"},{"key":"e_1_3_3_3_41_2","doi-asserted-by":"publisher","unstructured":"Sohail\u00a0Ahmed Khan Ghazaal Sheikhi Andreas\u00a0L. Opdahl Fazle Rabbi Sergej Stoppel Christoph Trattner and Duc-Tien Dang-Nguyen. 2023. Visual User-Generated Content Verification in Journalism: An Overview. IEEE Access 11 (2023) 6748\u20136769. 10.1109\/ACCESS.2023.3236993","DOI":"10.1109\/ACCESS.2023.3236993"},{"key":"e_1_3_3_3_42_2","doi-asserted-by":"publisher","unstructured":"Pawe\u0142 Korus and Jiwu Huang. 2017. Multi-Scale Analysis Strategies in PRNU-Based Tampering Localization. IEEE Transactions on Information Forensics and Security 12 4 (2017) 809\u2013824. 10.1109\/TIFS.2016.2636089","DOI":"10.1109\/TIFS.2016.2636089"},{"key":"e_1_3_3_3_43_2","unstructured":"Boquan Li Jun Sun Christopher\u00a0M. Poskitt and Xingmei Wang. 2024. How Generalizable are Deepfake Image Detectors? An Empirical Study. arxiv:https:\/\/arXiv.org\/abs\/2308.04177\u00a0[cs.CV]"},{"key":"e_1_3_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"e_1_3_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16419-4_44"},{"key":"e_1_3_3_3_46_2","doi-asserted-by":"crossref","unstructured":"Li Lin Neeraj Gupta Yue Zhang Hainan Ren Chun-Hao Liu Feng Ding Xin Wang Xin Li Luisa Verdoliva and Shu Hu. 2024. Detecting Multimedia Generated by Large AI Models: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2402.00045\u00a0[cs.MM]","DOI":"10.36227\/techrxiv.170723324.44685515\/v1"},{"key":"e_1_3_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437880.3460400"},{"key":"e_1_3_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-817636-8.00006-5"},{"key":"e_1_3_3_3_49_2","doi-asserted-by":"publisher","unstructured":"Asad Malik Minoru Kuribayashi Sani\u00a0M. Abdullahi and Ahmad\u00a0Neyaz Khan. 2022. DeepFake Detection for Human Face Images and Videos: A Survey. IEEE Access 10 (2022) 18757\u201318775. 10.1109\/ACCESS.2022.3151186","DOI":"10.1109\/ACCESS.2022.3151186"},{"key":"e_1_3_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICICT50521.2020.00051"},{"key":"e_1_3_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSPA57446.2023.10087398"},{"key":"e_1_3_3_3_52_2","doi-asserted-by":"publisher","unstructured":"Momina Masood Mariam Nawaz Khalid\u00a0Mahmood Malik Ali Javed Aun Irtaza and Hafiz Malik. 2022. Deepfakes generation and detection: state-of-the-art open challenges countermeasures and way forward. Applied Intelligence 53 4 (June 2022) 3974\u20134026. 10.1007\/s10489-022-03766-z","DOI":"10.1007\/s10489-022-03766-z"},{"key":"e_1_3_3_3_53_2","unstructured":"Matt Novak. 2023. That Viral Image Of Pope Francis Wearing A White Puffer Coat Is Totally Fake. https:\/\/www.forbes.com\/sites\/mattnovak\/2023\/03\/26\/that-viral-image-of-pope-francis-wearing-a-white-puffer-coat-is-totally-fake\/."},{"key":"e_1_3_3_3_54_2","doi-asserted-by":"publisher","unstructured":"Fatemeh\u00a0Zare Mehrjardi Ali\u00a0Mohammad Latif Mohsen\u00a0Sardari Zarchi and Razieh Sheikhpour. 2023. A survey on deep learning-based image forgery detection. Pattern Recognition 144 (2023). 10.1016\/j.patcog.2023.109778","DOI":"10.1016\/j.patcog.2023.109778"},{"key":"e_1_3_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47262-6_3"},{"key":"e_1_3_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00391"},{"key":"e_1_3_3_3_57_2","doi-asserted-by":"crossref","unstructured":"Robert\u00a0C Nickerson Upkar Varshney and Jan Muntermann. 2013. A method for taxonomy development and its application in information systems. European Journal of Information Systems 22 3 (2013) 336\u2013359.","DOI":"10.1057\/ejis.2012.26"},{"key":"e_1_3_3_3_58_2","unstructured":"Scott\u00a0W. O\u2019Connor. n. d.. What Does an Intelligence Analyst Do? https:\/\/graduate.northeastern.edu\/knowledge-hub\/what-does-an-intelligence-analyst-do\/. Accessed: 2025-01-24."},{"key":"e_1_3_3_3_59_2","unstructured":"Organization of Scientific Area Committees\u00a0(OSAC). n. d.. Video\/Imaging Technology & Analysis Subcommittee. https:\/\/www.nist.gov\/osac\/subcommittees\/videoimaging-technology-analysis. Accessed: 2024-11-13."},{"key":"e_1_3_3_3_60_2","unstructured":"Office of\u00a0the Director\u00a0of National\u00a0Intelligence. n. d.. Intelligence Analysis. https:\/\/www.intelligence.gov\/careers\/explore-careers\/389-intelligence-analysis. Accessed on 2025-01-27."},{"key":"e_1_3_3_3_61_2","unstructured":"Office of the Director of National Intelligence. 2022. Intelligence Community Directive 203 Technical Amendment. https:\/\/www.odni.gov\/files\/documents\/ICD\/ICD-203_TA_Analytic_Standards_21_Dec_2022.pdf."},{"key":"e_1_3_3_3_62_2","unstructured":"Office of the Director of National Intelligence. n. d.. What is Intelligence? https:\/\/www.dni.gov\/index.php\/what-we-do\/what-is-intelligence. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_63_2","unstructured":"Scientific Working\u00a0Group on Digital\u00a0Evidence(SWGDE). 2025. Published - Complete Listing. https:\/\/www.swgde.org\/published-complete-listing. Accessed: 2024-11-13."},{"key":"e_1_3_3_3_64_2","unstructured":"OpenAI. 2024. Hello GPT-4o. https:\/\/openai.com\/index\/hello-gpt-4o\/"},{"key":"e_1_3_3_3_65_2","first-page":"610","volume-title":"Advances in Neural Information Processing Systems (NeurIPS 2021)","author":"Paleja Rohan","year":"2021","unstructured":"Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka\u00a0Arachchige, Reed Jensen, and Matthew Gombolay. 2021. The Utility of Explainable AI in Ad Hoc Human-Machine Teaming. In Advances in Neural Information Processing Systems (NeurIPS 2021) , Vol.\u00a034. Curran Associates, Inc., Virtual, 610\u2013623."},{"key":"e_1_3_3_3_66_2","unstructured":"Ivan Perov Daiheng Gao Nikolay Chervoniy Kunlin Liu Sugasa Marangonda Chris Um\u00e9 Dpfks Carl\u00a0Shift Facenheim Luis RP Jian Jiang Sheng Zhang Pingyu Wu Bo Zhou and Weiming Zhang. 2021. DeepFaceLab: Integrated flexible and extensible face-swapping framework. arxiv:https:\/\/arXiv.org\/abs\/2005.05535\u00a0[cs.CV]"},{"key":"e_1_3_3_3_67_2","unstructured":"Samuele Pino Mark\u00a0James Carman and Paolo Bestagini. 2021. What\u2019s wrong with this video? Comparing Explainers for Deepfake Detection. arxiv:https:\/\/arXiv.org\/abs\/2105.05902\u00a0[cs.CV]"},{"key":"e_1_3_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"e_1_3_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"e_1_3_3_3_70_2","volume-title":"The Coding Manual for Qualitative Researchers","author":"Salda\u00f1a Johnny","year":"2021","unstructured":"Johnny Salda\u00f1a. 2021. The Coding Manual for Qualitative Researchers. SAGE, Thousand Oaks, CA, USA. 1\u2013440 pages."},{"key":"e_1_3_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_3_3_72_2","unstructured":"National Security Agency\/Central\u00a0Security Service. n. d.. NSA U.S. Federal Agencies Advise on Deepfake Threats. https:\/\/www.nsa.gov\/Press-Room\/Press-Releases-Statements\/Press-Release-View\/Article\/3523329\/nsa-us-federal-agencies-advise-on-deepfake-threats. Accessed: 2025-02-05."},{"key":"e_1_3_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494109.3527194"},{"key":"e_1_3_3_3_74_2","doi-asserted-by":"publisher","unstructured":"Nitin\u00a0Arvind Shelke and Singara\u00a0Singh Kasana. 2021. A comprehensive survey on passive techniques for digital video forgery detection. Multimedia Tools Appl. 80 4 (Feb. 2021) 6247\u20136310. 10.1007\/s11042-020-09974-4","DOI":"10.1007\/s11042-020-09974-4"},{"key":"e_1_3_3_3_75_2","unstructured":"Raquel V\u00e1zquez\u00a0Llorente shirin anlen. 2024. Spotting the deepfakes in this year of elections: how AI detection tools work and where they fail. https:\/\/reutersinstitute.politics.ox.ac.uk\/news\/spotting-deepfakes-year-elections-how-ai-detection-tools-work-and-where-they-fail."},{"key":"e_1_3_3_3_76_2","doi-asserted-by":"publisher","unstructured":"Leslie\u00a0F. Sikos. 2021. AI in digital forensics: Ontology engineering for cybercrime investigations. WIREs Forensic Science 3 3 (2021) 11\u00a0pages. 10.1002\/wfs2.1394","DOI":"10.1002\/wfs2.1394"},{"key":"e_1_3_3_3_77_2","volume-title":"Computation + Journalism Symposium","author":"Sohrawardi Saniat\u00a0Javid","year":"2020","unstructured":"Saniat\u00a0Javid Sohrawardi, Akash Chintha, Bao Thai, Sovantharith Seng, Andrea Hickerson, Raymond Ptucha, and Matthew Wright. 2020. DeFaking Deepfakes: Understanding Journalists\u2019 Needs for Deepfake Detection. In Computation + Journalism Symposium. C+J, Boston, MA, USA, 5\u00a0pages."},{"key":"e_1_3_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3641973"},{"key":"e_1_3_3_3_79_2","unstructured":"TALENTKINGHD. 2023. America\u2019s Got Talent 2022 Metaphysic Finals Full Performance & Intro. https:\/\/www.youtube.com\/watch?v=nHDYpxYP6sk."},{"key":"e_1_3_3_3_80_2","doi-asserted-by":"publisher","unstructured":"Diangarti Tariang Riccardo Corvi Davide Cozzolino Giovanni Poggi Koki Nagano and Luisa Verdoliva. 2024. Synthetic Image Verification in the Era of Generative Artificial Intelligence: What Works and What Isn\u2019t There yet. IEEE Security & Privacy 22 03 (May 2024) 37\u201349. 10.1109\/MSEC.2024.3376637","DOI":"10.1109\/MSEC.2024.3376637"},{"key":"e_1_3_3_3_81_2","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Yonghui Wu Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew\u00a0M Dai Anja Hauth et\u00a0al. 2024. Gemini: a family of highly capable multimodal models. arxiv:https:\/\/arXiv.org\/abs\/2312.11805\u00a0[cs.CL]"},{"key":"e_1_3_3_3_82_2","doi-asserted-by":"publisher","unstructured":"Rahul Thakur and Rajesh Rohilla. 2020. Recent advances in digital image manipulation detection techniques: A brief review. Forensic Science International 312 (2020). 10.1016\/j.forsciint.2020.110311","DOI":"10.1016\/j.forsciint.2020.110311"},{"key":"e_1_3_3_3_83_2","doi-asserted-by":"publisher","unstructured":"Alice Toniolo Federico Cerutti Timothy\u00a0J. Norman Nir Oren John\u00a0A. Allen Mani Srivastava and Paul Sullivan. 2023. Human-machine collaboration in intelligence analysis: An expert evaluation. Intelligent Systems with Applications 17 (2023). 10.1016\/j.iswa.2022.200151","DOI":"10.1016\/j.iswa.2022.200151"},{"key":"e_1_3_3_3_84_2","unstructured":"TrueMedia. 2024. Identifying Political Deepfakes in Social Media using AI. https:\/\/www.truemedia.org\/. https:\/\/www.truemedia.org\/ Accessed: 2024-08-14."},{"key":"e_1_3_3_3_85_2","doi-asserted-by":"publisher","unstructured":"Luisa Verdoliva. 2020. Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing 14 5 (2020) 910\u2013932. 10.1109\/JSTSP.2020.3002101","DOI":"10.1109\/JSTSP.2020.3002101"},{"key":"e_1_3_3_3_86_2","unstructured":"James Vincent. 2019. Deepfake detection algorithms will never be enough. https:\/\/www.theverge.com\/2019\/6\/27\/18715235\/deepfake-detection-ai-algorithms-accuracy-will-they-ever-work"},{"key":"e_1_3_3_3_87_2","doi-asserted-by":"publisher","unstructured":"Tianyi Wang Harry Cheng Kam\u00a0Pui Chow and Liqiang Nie. 2023. Deep convolutional pooling transformer for deepfake detection. ACM Trans. Multimedia Comput. Commun. Appl. 19 6 (2023) 1\u201320. 10.1145\/3588574","DOI":"10.1145\/3588574"},{"key":"e_1_3_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW54805.2022.00044"},{"key":"e_1_3_3_3_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00858"},{"key":"e_1_3_3_3_90_2","first-page":"4534","volume-title":"Advances in Neural Information Processing Systems","author":"Yan Zhiyuan","year":"2023","unstructured":"Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, and Baoyuan Wu. 2023. DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection. In Advances in Neural Information Processing Systems , Vol.\u00a036. Curran Associates, Inc., New Orleans, USA, 4534\u20134565. https:\/\/neurips.cc\/virtual\/2023\/poster\/73502"},{"key":"e_1_3_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"e_1_3_3_3_92_2","doi-asserted-by":"publisher","unstructured":"Mohammed Zakariah Muhammad\u00a0Khurram Khan and Hafiz Malik. 2018. Digital multimedia audio forensics: past present and future. Multimedia Tools Appl. 77 1 (Jan. 2018) 1009\u20131040. 10.1007\/s11042-016-4277-2","DOI":"10.1007\/s11042-016-4277-2"},{"key":"e_1_3_3_3_93_2","unstructured":"He Zhang Chuhao Wu Jingyi Xie Yao Lyu Jie Cai and John\u00a0M. Carroll. 2024. Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis. arxiv:https:\/\/arXiv.org\/abs\/2309.10771\u00a0[cs.HC]"},{"key":"e_1_3_3_3_94_2","doi-asserted-by":"publisher","unstructured":"Cairong Zhao Chutian Wang Guosheng Hu Haonan Chen Chun Liu and Jinhui Tang. 2023. ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection. IEEE Transactions on Information Forensics and Security 18 (2023) 1335\u20131348. 10.1109\/TIFS.2023.3239223","DOI":"10.1109\/TIFS.2023.3239223"}],"event":{"name":"CHI 2025: CHI Conference on Human Factors in Computing Systems","location":"Yokohama Japan","acronym":"CHI '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706598.3713711","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706598.3713711","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706598.3713711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:26:30Z","timestamp":1751606790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706598.3713711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":93,"alternative-id":["10.1145\/3706598.3713711","10.1145\/3706598"],"URL":"https:\/\/doi.org\/10.1145\/3706598.3713711","relation":{},"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"2025-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}