{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T07:36:40Z","timestamp":1775547400097,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this work, we created a new large-scale unconstrained high-quality Deepfake Image (DFIM-HQ) dataset containing 140K images. Compared to existing datasets, this dataset includes a variety of diverse scenarios, pose variations, high-quality degradations, and illumination variations, making it a particularly challenging dataset. Since computer vision models learn to perform a task by capturing relevant statistics from training data, they tend to learn spurious age, gender, and race correlations leading to learning biases. To account for AI bias in our proposed DFIM-HQ dataset, we design a simple yet effective image recognition benchmark for studying bias mitigation. Our detection system makes use of an Inception-based network to extract frame-level features and automatically detect manipulated content. We also propose an explainability framework that provides a better understanding of the model\u2019s prediction. Such informed decisions provide insights that can be used to improve the model and, thereby, helps to add trust to the model. Our evaluation illustrates that our frameworks can achieve competitive results in detecting deepfake images using deep learning architectures.<\/jats:p>","DOI":"10.1007\/s40747-022-00956-7","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T04:39:21Z","timestamp":1673930361000},"page":"4425-4437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An explainable deepfake detection framework on a novel unconstrained dataset"],"prefix":"10.1007","volume":"9","author":[{"given":"Sherin","family":"Mathews","sequence":"first","affiliation":[]},{"given":"Shivangee","family":"Trivedi","sequence":"additional","affiliation":[]},{"given":"Amanda","family":"House","sequence":"additional","affiliation":[]},{"given":"Steve","family":"Povolny","sequence":"additional","affiliation":[]},{"given":"Celeste","family":"Fralick","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"956_CR1","unstructured":"Eyerys, An AI Capable In Creating Fake Porn, Is Starring Gal Gadot And More: A Terrifying Implication https:\/\/www.eyerys.com\/articles\/news\/ai-capable-creating-fake-porn-starring-gal-gadot-and-more-terrifying-implication"},{"key":"956_CR2","unstructured":"Cnet, Jordan Peele turns Obama into foul-mouthed fake-news PSA. https:\/\/www.cnet.com\/news\/jordan-peele-buzzfeed-turn-obama-into-foul-mouthed-fake-news-psa\/"},{"key":"956_CR3","unstructured":"Motherboard, This Deepfake of Mark Zuckerberg Tests Facebook\u2019s Fake Video Policies, https:\/\/www.wsj.com\/articles\/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402"},{"key":"956_CR4","unstructured":"The Wall Street Journal, Fraudsters Used AI to Mimic CEO\u2019s Voice in Unusual Cybercrime Case, https:\/\/www.wsj.com\/articles\/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402"},{"key":"956_CR5","unstructured":"Knight W. AI-powered text from this program could fool the government [Internet]. Wired. Conde Nast; 2021 [cited 2021Apr29]. Available from: https:\/\/www.wired.com\/story\/ai-powered-text-program-could-fool-government\/"},{"key":"956_CR6","unstructured":"Photo Tampering Throughout History. https:\/\/web.archive.org\/web\/20150908155915\/, http:\/\/www.cc.gatech.edu\/~beki\/cs4001\/history.pdf"},{"key":"956_CR7","unstructured":"FakeApp. https:\/\/www.malavida.com\/en\/soft\/fakeapp\/"},{"key":"956_CR8","unstructured":"Faceswap. https:\/\/github.com\/deepfakes\/faceswap#deepfakesfaceswap"},{"key":"956_CR9","unstructured":"Dfaker. https:\/\/github.com\/dfaker\/df"},{"key":"956_CR10","unstructured":"Petrov I, Gao D, Chervoniy N, Liu K, Marangonda S, Um\u00e9 C, Jiang J, RP L, Zhang S, Wu P, Zhang W (2020) Deepfacelab: a simple, flexible and extensible face swapping framework. arXiv preprint arXiv:2005.05535"},{"key":"956_CR11","unstructured":"DeepFaceLab. https:\/\/github.com\/iperov\/DeepFaceLab"},{"key":"956_CR12","unstructured":"Introducing the new SAEHD model. https:\/\/www.reddit.com\/r\/SFWdeepfakes\/comments \/dfgv68\/introducing the new saehd model\/"},{"key":"956_CR13","doi-asserted-by":"crossref","unstructured":"Thies J, Zollhofer M, Stamminger M, Theobalt C, Nie\u00dfner M (2016) Face2face: Real-time face capture and reenactment of rgb videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2387-2395)","DOI":"10.1109\/CVPR.2016.262"},{"key":"956_CR14","unstructured":"https:\/\/lingzhili.com\/FaceShifterPage\/"},{"issue":"64","key":"956_CR15","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","volume":"1","author":"R Tolosana","year":"2020","unstructured":"Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A, Ortega-Garcia J (2020) Deepfakes and beyond: a survey of face manipulation and fake detection. Inform Fus 1(64):131\u201348","journal-title":"Inform Fus"},{"key":"956_CR16","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"956_CR17","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196"},{"key":"956_CR18","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8789-8797)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"956_CR19","doi-asserted-by":"crossref","unstructured":"Li Y, Chang MC, Lyu S (2018) In ictu oculi: exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1-7). IEEE","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"956_CR20","unstructured":"Li Y, Lyu S (2018) Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656"},{"key":"956_CR21","doi-asserted-by":"crossref","unstructured":"Zhou P, Han X, Morariu VI, Davis LS (2017) Two-stream neural networks for tampered face detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 1831-1839). IEEE","DOI":"10.1109\/CVPRW.2017.229"},{"key":"956_CR22","doi-asserted-by":"crossref","unstructured":"Yang X, Li Y, Qi H, Lyu S (2019) Exposing gan-synthesized faces using landmark locations. In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security (pp. 113-118)","DOI":"10.1145\/3335203.3335724"},{"issue":"174","key":"956_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107616","volume":"1","author":"H Li","year":"2020","unstructured":"Li H, Li B, Tan S, Huang J (2020) Identification of deep network generated images using disparities in color components. Signal Process 1(174):107616","journal-title":"Signal Process"},{"key":"956_CR24","unstructured":"Agarwal S, Farid H, Gu Y, He M, Nagano K, Li H (2019) Protecting world leaders against deep fakes. InCVPR Workshops (pp. 38-45)"},{"key":"956_CR25","unstructured":"https:\/\/www.darpa.mil\/program\/media-forensics"},{"key":"956_CR26","unstructured":"https:\/\/ai.facebook.com\/datasets\/dfdc\/"},{"key":"956_CR27","unstructured":"Dolhansky B, Bitton J, Pflaum B, Lu J, Howes R, Wang M, Ferrer CC (2020) The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397"},{"key":"956_CR28","unstructured":"https:\/\/niessnerlab.org\/projects\/roessler2019faceforensicspp.html"},{"key":"956_CR29","doi-asserted-by":"crossref","unstructured":"Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M. (2019) Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 1-11)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"956_CR30","unstructured":"https:\/\/github.com\/socialabubi\/iFakeFaceDB"},{"key":"956_CR31","unstructured":"iFakeFacesDB Dataset. https:\/\/github.com\/socialabubi\/iFakeFaceDB"},{"key":"956_CR32","unstructured":"Notre Dame Synthetic Face Dataset. https:\/\/cvrl.nd.edu\/projects\/data\/"},{"key":"956_CR33","doi-asserted-by":"crossref","unstructured":"Banerjee S, Scheirer WJ, Bowyer KW, Flynn PJ (2019) Fast face image synthesis with minimal training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 2126-2136). IEEE","DOI":"10.1109\/WACV.2019.00230"},{"key":"956_CR34","unstructured":"http:\/\/cvlab.cse.msu.edu\/dffd-dataset.html"},{"key":"956_CR35","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK (2020) On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 5781-5790)","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"956_CR36","doi-asserted-by":"crossref","unstructured":"Guan H, Kozak M, Robertson E, Lee Y, Yates AN, Delgado A, Zhou D, Kheyrkhah T, Smith J, Fiscus J (2019) MFC datasets: large-scale benchmark datasets for media forensic challenge evaluation. In: 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) Jan 7 (pp. 63-72). IEEE","DOI":"10.1109\/WACVW.2019.00018"},{"key":"956_CR37","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2018)Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179"},{"key":"956_CR38","doi-asserted-by":"crossref","unstructured":"Neves JC, Tolosana R, Vera-Rodriguez R, Lopes V, Proen\u00e7Sa H, Fierrez J (2020) Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection. IEEE J Select Top Signal Process 14(5):1038-1048","DOI":"10.1109\/JSTSP.2020.3007250"},{"key":"956_CR39","doi-asserted-by":"crossref","unstructured":"Zi B, Chang M, Chen J, Ma X, Jiang YG (2020) WildDeepfake: a challenging real-world dataset for deepfake detection. In: Proceedings of the 28th ACM International Conference on Multimedia (pp. 2382-2390)","DOI":"10.1145\/3394171.3413769"},{"key":"956_CR40","unstructured":"http:\/\/cvlab.cse.msu.edu\/project-ffd.html"},{"key":"956_CR41","unstructured":"LFW dataset. http:\/\/vis-www.cs.umass.edu\/lfw\/"},{"key":"956_CR42","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK (2020) On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 5781-5790)","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"956_CR43","unstructured":"FFD Dataset. http:\/\/cvlab.cse.msu.edu\/project-ffd.html"},{"key":"956_CR44","doi-asserted-by":"crossref","unstructured":"Serengil SI, Ozpinar A (2020) LightFace: a hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) Oct 15 (pp. 1-5). IEEE","DOI":"10.1109\/ASYU50717.2020.9259802"},{"key":"956_CR45","doi-asserted-by":"crossref","unstructured":"Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, Martino J, Mehta S, Mojsilovic A, Nagar S (2018) AI Fairness 360: an extensible toolkit for detecting. Understanding, and Mitigating Unwanted Algorithmic Bias","DOI":"10.1147\/JRD.2019.2942287"},{"key":"956_CR46","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386"},{"key":"956_CR47","doi-asserted-by":"crossref","unstructured":"Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) Oct 1 (pp. 80-89). IEEE","DOI":"10.1109\/DSAA.2018.00018"},{"key":"956_CR48","doi-asserted-by":"crossref","unstructured":"Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B (2019) Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592","DOI":"10.1073\/pnas.1900654116"},{"key":"956_CR49","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision (pp. 618-626)","DOI":"10.1109\/ICCV.2017.74"},{"key":"956_CR50","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016)\u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining Aug 13 (pp. 1135-1144)","DOI":"10.1145\/2939672.2939778"},{"key":"956_CR51","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921-2929)","DOI":"10.1109\/CVPR.2016.319"},{"key":"956_CR52","doi-asserted-by":"crossref","unstructured":"Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS) Dec 11 (pp. 1-7). IEEE","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"956_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1\u20139)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"956_CR54","doi-asserted-by":"crossref","unstructured":"Mathews SM (2019) Explainable artificial intelligence applications in NLP, biomedical, and malware classification: a literature review. In: Intelligent computing-proceedings of the computing conference. Springer, Cham, pp 1269\u20131292","DOI":"10.1007\/978-3-030-22868-2_90"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00956-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-022-00956-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00956-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T13:28:28Z","timestamp":1690464508000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-022-00956-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["956"],"URL":"https:\/\/doi.org\/10.1007\/s40747-022-00956-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]},"assertion":[{"value":"4 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}