[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-151704-en":3,"doc-seo-151704-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},151704,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The Point Where Reality Meets Fantasy - Mixed Adversarial Generators for Image Splice Detection","Modern photo editing tools enable highly realistic image manipulations, yet training reliable detection models is difficult because tampering artifacts vary widely and large labeled datasets of manipulated images are limited. This paper introduces a Mixed Adversarial Generators framework that jointly trains retouching, annotation, and discriminator models for adversarial segmentation-based splice detection. A large dataset with pixel-level annotations is collected and used for training, and extensive evaluations validate improved accuracy over state-of-the-art methods.","The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection  \nVladimir V. Kniaz 1 ;2 , Vladimir A. Knyaz 1 ;2  \n1 State Res. Institute of Aviation Systems (GosNIIAS)  \n125319, 7, Victorenko str., Moscow, Russia {vl.kniaz, [knyaz}@gosniias.ru](knyaz}@gosniias.ru)  \n2Moscow Institute of Physics and Technology (MIPT)  \n141701, 9 Institutskiy per., Dolgoprudny, Russia  \nFabio Remondino  \nFondazione Bruno Kessler (FBK) Via Sommarive 18, Trento, Italy [remondino@fbk.eu](remondino@fbk.eu)  \nAbstract  \nModern photo editing tools allow creating realistic manipulated images easily.  \nWhile fake images can be quickly generated, learning models for their detection is challenging due to the high variety of tampering artifacts and the lack of large labeled datasets of manipulated images. In this paper, we propose a new framework for training of discriminative segmentation model via an adversarial process. We simultaneously train four models: a generative retouching model GR that translates manipulated image to the real image domain, a generative annotation model GA that estimates the pixel-wise probability of image patch being either real or fake, and two discriminators DR and DA that qualify the output of GR and GA . The aim of model GR is to maximize the probability of model GA making a mistake. Our method extends the generative adversarial networks framework with two main contributions:  \n(1) training of a generative model GR against a deep semantic segmentation network GA that learns rich scene semantics for manipulated region detection,(2) proposing per class semantic loss that facilitates semantically consistent image retouching by the GR . We collected large-scale manipulated image dataset to train our model. The dataset includes 16k real and fake images with pixel-level annotations of manipulated areas. The dataset also provides ground truth pixellevel object annotations. We validate our approach on several modern manipulated image datasets, where quantitative results and ablations demonstrate that our method achieves and surpasses the state-of-the-art in manipulated image detection. We made our code and dataset publicly available 1.  \n1 Introduction  \nWhile every image captured by the human eye is real, digital photos can be easily manipulated to present scenes that never existed in reality. Such manipulated image can be easily generated by copying the part of one image into another. This image manipulation is called an image splice and can be used maliciously to create fake news or change historical photos [1] . Recent research [1, 2] suggests that training a model for splice localization is more challenging than other types of object detection problems as the domain of manipulated images is extensive and diverse. Therefore, the collection of the representative training dataset is difficult. Moreover, the forger can adapt to the detection algorithm by changing the manipulation technique. This principle is used in Generative Adversarial Networks (GANs) to train a generator network to synthesize images from noise [3], text descriptions [4], scene graphs [5] or by image-to-image translation [6, 7, 8, 9] . Fake images produced by the generator are evaluated against real images by an adversarial discriminator network that learns  \n1[http://zefirus.org/MAG](http://zefirus.org/MAG)  \n33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.  \nManipulated Image Ground Truth CFA [10] LSC [1] Ours  \nFigure 1: Comparison against two state-of-the-art methods on our FantasticReality dataset (Section 3 .3) . Our results are shown in the last column. Zoom in for details.  \nto classify them as `real' or `fake.' Even though no `fake' images exist in the training dataset, the discriminator successfully learns to detect them during the training process. We hypothesize that adversarial training of an image-to-image translation generator against a splice localization generator can imp","cbCailq4ticHM4ZC","https://ap.wps.com/l/cbCailq4ticHM4ZC","pdf",13518814,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address in image splice detection?\",\"answer\":\"It targets the challenge of learning to localize image splices despite diverse tampering artifacts and limited large-scale labeled datasets.\"},{\"question\":\"How does the proposed MAG framework work conceptually?\",\"answer\":\"It simultaneously trains a retoucher generator GR to suppress artifacts, a generative annotator GA to predict splice localization masks on the retouched images, and discriminators to evaluate outputs, using adversarial losses to improve detection.\"},{\"question\":\"What are the main contributions claimed by the paper?\",\"answer\":\"The method includes adversarial training of GR against a semantic segmentation-based GA and introduces per-class semantic loss to enforce semantically consistent image retouching.\"}]","The Point Where Reality Meets Fantasy - Mixed Adversarial Generators for Image Splice Detection | PDF",1787847724,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-point-where-reality-meets-fantasy-mixed-adversarial-generators-for-image-splice-detection","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-point-where-reality-meets-fantasy-mixed-adversarial-generators-for-image-splice-detection/151704/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-05","2026-08-27",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in image splice detection?","Question",{"text":76,"@type":77},"It targets the challenge of learning to localize image splices despite diverse tampering artifacts and limited large-scale labeled datasets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed MAG framework work conceptually?",{"text":81,"@type":77},"It simultaneously trains a retoucher generator GR to suppress artifacts, a generative annotator GA to predict splice localization masks on the retouched images, and discriminators to evaluate outputs, using adversarial losses to improve detection.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the main contributions claimed by the paper?",{"text":85,"@type":77},"The method includes adversarial training of GR against a semantic segmentation-based GA and introduces per-class semantic loss to enforce semantically consistent image retouching.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]