[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124230-en":3,"doc-seo-124230-105":30,"detail-sidebar-cat-0-en-105":91},{"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},124230,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Literature Survey of Image Forgery Detection Using Machine Learning - Master’s Thesis - April 2025","The rapid improvement in digital image manipulation has made image forgery detection essential for authenticity and security, especially as artificial deepfakes and adversarially altered images become more common. This thesis surveys machine learning-based technologies, emphasizing deep learning models, hybrid AI, and emerging security approaches such as Explainable AI, blockchain-verified authentication, and self-supervised learning. It reviews state-of-the-art active and passive methods and highlights open issues including generalization limits, adversarial robustness, real-time efficiency, and dataset constraints, while outlining directions for future work.","Sirajum Monira Bipasha  \nLiterature Survey of Image Forgery Detection Using Machine Learning.  \nMetropolia University of Applied Sciences  \nMaster of Engineering Information Technology Master’s Thesis  \nApril 2025  \nPreface  \nFirst and foremost, I would want to thank God for His grace and guidance which have granted me the strength and determination to strive and complete this research. This thesis,\"Literature Survey of Image Forgery Detection using Machine Learning,\" is the fruit of my dedication and passion in seeking the challenges and achievements of digital image forensics.  \nThe increased prevalence of image forgery in the current digital era has created authenticity and security-related concerns. The current work provides an extensive overview of machine learning-based technologies to detect forgery, such as deep learning methods, selfsupervised learning, and blockchain-verified authentication. It also indicates the key challenges, such as generalization issues, adversarial attacks, and computational complexity, and provides directions for future work.  \nI am personally most grateful to my husband, whose encouragement, support, and patience have made a big difference to me at every step of the way. My children are my inspiration and source of delight, reminding me always of the importance of never giving up. I am personally most grateful, also, to my parents whose guidance, prayer, and affection formed me as an individual that I am.  \nI hope this work helps to add to the increasing body of image forgery detection and is a valuable resource for researchers in the future.  \nFinland , Date Sirajum Monira  \nAuthor: Sirajum Monira  \nTitle: Literature Survey of Image Forgery Detection Using Machine  \nLearning  \nNumber of Pages: 47 pages  \nDate: April 2025  \nDegree: Master of Engineering  \nDegree Programme: Information Technology  \nProfessional Major: Networking and Services  \nSupervisors: Ville Jääskeläinen , Head of Master’s in IT  \nAbstract: The rapid improvement in digital image manipulation means image forgery detection is more needed now than it ever was-be it artificially intelligently created deepfakes or adversarial altered images. Traditional techniques for error level analysis, copy-move detection, and splicing detection are too narrow to help forensics in keeping up with today's sophisticated AI-driven forgeries. The contribution of this survey is that it provides a detailed review of state-of-the-art machine learning-based forgery detection methods with particular emphasis on deep learning models, hybrid AI, and new emerging security technologies, such as Explainable AI, blockchain, and self-supervised learning. In general, approaches for image forgery detection may be classified as active or passive. This investigates the performance of state-of-the-art forgery detection techniques, ranging from feature-based machine learning classifiers (SVM, k-NN, and Decision Trees) to deep learning models, including CNNs and Vision Transformers. Yet, despite this progress, generalization, adversarial robustness, real-time efficiency, and dataset limitations are among the most well-known open issues that impede large-scale adoption. Among these, this investigate introduce the role of XAI methods such as Grad-CAM and SHAP in deep learning that can provide more transparent and interpretable deep learning models, an essential factor for forensic and legal applications. Going beyond deep learning, it explores blockchain-based image verification that allows decentralized and tamper-proof tracking of image authenticity. Besides, SSL and FSL are discussed as promising techniques that enable forgery detection models to learn new types of image manipulations with minimal labeled data. While deep learning models achieved impressive accuracy rates, their high computational demands raise challenges toward real-time deployment, thus opening space for research into lightweight AI models, federated learning, and edge computing solutions. Hyb","cbCaitk88QoZkSky","https://ap.wps.com/l/cbCaitk88QoZkSky","pdf",992337,1,64,"English","en",105,"# 1 Introduction\n## 1.1 Background & Motivation\n## 1.2 Problem Statement\n## 1.3 Research Objectives\n## 1.4 Thesis Structure\n# 2 Image Forgery Methods and Types\n## 2.1 Active Image Forgery Methods\n## 2.2 Passive Image Forgery Methods\n## 2.2.1 Copy-Move Forgery\n## 2.2.2 Splicing Forgery","[{\"question\":\"How does the thesis classify image forgery detection approaches?\",\"answer\":\"It classifies approaches as active or passive, then reviews methods across feature-based classifiers and modern deep learning models.\"},{\"question\":\"What key challenges are highlighted for current forgery detection systems?\",\"answer\":\"Generalization issues, adversarial attacks (robustness), computational complexity affecting real-time use, and dataset limitations are identified as major open problems.\"},{\"question\":\"What future directions does the thesis suggest beyond standard deep learning?\",\"answer\":\"It discusses lightweight and efficient AI models, federated learning and edge computing, explainable AI for interpretability, blockchain-based verification, and self-supervised or few-shot learning to reduce labeled data needs.\"}]","Literature Survey of Image Forgery Detection Using Machine Learning - Master’s Thesis - April 2025 | PDF",1785821136,161,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"literature-survey-of-image-forgery-detection-using-machine-learning-masters-thesis-april-2025","",{"@graph":36,"@context":85},[37,54,68],{"@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/literature-survey-of-image-forgery-detection-using-machine-learning-masters-thesis-april-2025/124230/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis classify image forgery detection approaches?","Question",{"text":75,"@type":76},"It classifies approaches as active or passive, then reviews methods across feature-based classifiers and modern deep learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What key challenges are highlighted for current forgery detection systems?",{"text":80,"@type":76},"Generalization issues, adversarial attacks (robustness), computational complexity affecting real-time use, and dataset limitations are identified as major open problems.",{"name":82,"@type":73,"acceptedAnswer":83},"What future directions does the thesis suggest beyond standard deep learning?",{"text":84,"@type":76},"It discusses lightweight and efficient AI models, federated learning and edge computing, explainable AI for interpretability, blockchain-based verification, and self-supervised or few-shot learning to reduce labeled data needs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"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":106,"slug":138},19,"General","general"]