Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions

2026
Book

Editors: Abhishek Kumar; Dr. Priya Batta; T. Ananth Kumar; S. Oswalt Manoj
Book Series: Blockchain Technologies
Publisher: Springer Nature Singapore
Part of: Springer Professional "Business + Economics & Engineering + Technology"; Springer Professional "Engineering + Technology"; Springer Professional "Business + Economics"

About this book

This book highlights the transformative synergy between Blockchain and Federated Learning in developing privacy-focused solutions for DeepFex. By leveraging the decentralized nature of blockchain alongside the privacy-preserving capabilities of federated learning, it offers a novel approach to combating the growing challenges of deepfake technology. The integration of these two cutting-edge technologies ensures data security, model integrity, and transparent collaboration, making it possible to detect and mitigate deepfakes in a scalable, ethical, and decentralized manner.

Frontmatter

Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions

Abstract

The rapid development of deepfake technology poses a serious challenge to the legitimacy of digital media and requires effective detection techniques. This chapter explores the challenges and possible solutions of deepfake detection. The challenges encountered involve a lack of a diverse and high-quality dataset to train models, issues with scalability for real-world applications, and constantly evolving deepfake-creation techniques overtaking detection capabilities. Detection also has challenges generalizing to varied types of manipulation and complex in-the-wild scenarios where multiple individuals or changing environments exist. This chapter presents several novel avenues for the resolution of the specified issues. Enhanced cross-forgery detection using advanced architectural designs, like hybrid models combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), is realized at a larger scale. Detection accuracy is enhanced by multimodal fusion techniques, which integrate visual, aural, and contextual data. Attention mechanisms and other temporal analyses identify differences within the video sequences. In addition, persistent effectiveness against evolving techniques in deepfake manipulation while improving real-time detection efficiency provides an edge to computing. These research outcomes advance the development and fortification of deepfake detection technologies within a global framework that supports digital trust and curtails the circulation of synthetic media.

M. Harishmaa, S. Janani, K. A. Jayashree, J. Rufina Sherin, R. Manikandan, S. Magesh

DeepFex: Detecting DeepFakes in the Digital Age

Abstract

Deepfake technology employs advanced artificial intelligence to craft counterfeit videos, images, or audio that appear remarkably authentic, raising concerns about misinformation, privacy violations, and erosion of societal trust. This chapter explores innovative methods for identifying deepfakes using Federated Learning (FL) and blockchain, which collaborate to deliver secure, confidential, and adaptable detection solutions. With FL, devices such as smartphones train detection models locally without disclosing personal data, while blockchain securely logs model updates and outcomes to prevent manipulation. These strategies address challenges like safeguarding data, processing vast content volumes, and detecting sophisticated deepfakes. Applications span verifying media authenticity, securing financial transactions, ensuring healthcare integrity, promoting transparent governance, and protecting social media. This chapter also examines ethical dilemmas, such as biased model performance, and regulatory obstacles impacting deployment. It provides a strategic roadmap for deepfake detection, aiming to assist researchers, industry professionals, and policymakers in developing equitable, dependable solutions for a safer digital ecosystem.

Chaitanya Singla, Manjot Kaur Sidhu, Gurpreet Singh, Ravneet Kaur, Thuseethan Selvarajah

A Cross-Architecture Evaluation: CNN, LSTM, GANs, and Transformer Models for DeepFake Detection

Abstract

Deepfake is a technology for generating fake news or fake media based on images, videos, or audio that look genuine but are fake or synthetic. But this raises a bigger question—how to know that the image, video, or audio is not fraudulent and is not designed to mislead and manipulate people. Deepfakes can have deadly consequences, altering reality and eroding confidence in the media. The study provides an assessment of various deep learning techniques to detect deepfakes. Positive and negative aspects of deep learning are examined: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks, Generative Adversarial Networks (GANs), and transformer models. In this study, the optimal way of detecting deepfakes has been examined, and assessed whether implementing hybrid methods with various detection methods aids in expediting the process. Through this comparative analysis of these techniques, a generous contribution to the field of deepfake detection is made, providing a robust and accurate means of detecting manipulated content.

Arvinder Kaur, Suryansh Rana, Paryag Sahni, Amanpreet Kaur

DeepFex: Explainable and Secure Deepfake Detection System

Abstract

Modern digital media develops artificial intelligence technologies combined with deep learning features rapidly. The numerous uses of deepfake technologies raise serious questions as they supply exact reproductions of actual human characteristics from seem-to-speak and physical movement, so permitting deepfakes generation. Although deepfakes provide creative possibilities and entertainment value, they also bring ethical dilemmas, as well as social challenges and safety issues.

R. Karthick Manoj, S. Aasha Nandhini

Federated Learning with Blockchain-Enhanced Model Explainability

Abstract

Federated learning is a key methodology for decentralized training in machine learning under the paradigm of privacy preservation. However, many debilitating issues threaten its viability, primarily concerning model accountability and transparency, difficulties in making the model explainable and reliable. In this chapter, we focus on analyzing advances made in blockchain-aided federated learning systems to deal with these contrasting issues. Because of its immutable and decentralized nature, blockchain technology is touted as providing transparency and accountability to federated learning systems. The blockchain enables the recording and confirmation of model updates, decision-making processes, and causal links between participants, resulting in the provision of a tamper-proof audit trail. This ability allows stakeholders to monitor model behavior, verify contributions, and identify biases or adversarial activities. The blockchain-federated learning partnership will help researchers build more ethical, transparent, and scalable artificial intelligence systems. This chapter assesses the advances made so far, including a brief introduction to federated learning, an outline of state-of-the-art innovations in the blockchain-based federated learning systems, an analysis of key issues faced by federated learning and blockchain-enhanced federated learning, and possibilities of further enhancement in reliability and accountability. The goal of this chapter is to further the ongoing efforts aimed at addressing the technological and ethical issues pervading the era of distributed intelligence.

Gurjot Kaur, Vikas Wasson, Simarpreet Kaur

Advanced Privacy Measures for Data Sharing in Federated Learning Networks

Abstract

Federated learning (FL) is a promising mechanism for privacy-sensitive applications, as it enables cooperation to train models among many dispersed devices without sending raw data outside. More elaborate privacy-preserving solutions are needed since the current FL principles are subject to model inversion attacks, adversarial manipulations, and data leaking. Using blockchain technology, this research examines hybrid homomorphic encryption (HHE), dynamic differential privacy (DDP), and decentralized trust verification (DTV) to study adaptive privacy-preserving federated learning (APFL). While the traditional privacy-preserving FL models are rigid in terms of privacy parameters and do not define how to integrate or change those privacy levels, APFL is built to dynamically change privacy parameters relative to immediate threats for a better balance in security and model performance. Literature review yields a clear understanding of the gap in security of current FL models compared with high-dimensional data privacy leakage and the adaptive inference attacks. Unlike most privacy-preserving approaches, in part because it adopts the adaptive privacy budget allocation, APFL endorses selective adjustments in noise levels according to relative sensitivity before applying DDP to reduce unwarranted loss of accuracy. Encrypted model updates can be made possible with a computational overhead lower than 68% that of conventional HE systems through the HHE mechanism. Moreover, a DTV powered by blockchain secures model aggregate against unauthorized changes and guarantees 99.8% data integrity. This framework was tested extensively with several privacy settings on MNIST and CIFAR-10 datasets. Evaluation results showed that APFL is superior in terms of both security and performance stability over traditional FL approaches, with accuracies of 94.2% and 98.6% on CIFAR-10 and MNIST, respectively, with a privacy budget (ε) of 1.5. Furthermore, APFL showed 24% higher computation efficiencies with a 72% loss of information leakage, making it fit for those real-world applications like smart city data analysis, financial fraud detection, and healthcare diagnostics. Fixing those significant FL deficiencies is innovative, flexible, and extraordinarily effective in ensuring the privacy of applications such as human studies.

P. Manju Bala, S. Usharani, A. Balachandar, A. Olukayode

Enhanced Privacy Preserving Deep Learning Using Blockchain and Federated Learning

Abstract

In order to protect model updates, prevent any malicious attempts, and increase computing efficiency, this research aims to provide a highly secure, privacy-preserving, and attack-resistant federated learning (FL) framework empowered by blockchain. This gives the cause for security to organize decentralized frameworks of deep learning, uniquely denoting the vulnerabilities that come with threats associated with existing FL methods: gradient leakage, model poisoning, Byzantine errors, and blockchain scalability bounds. Traditional methods suffer from limitations in real-world applicability due to halfway methods that either put a significant load on computation or deny data privacy. This chapter proposes the design of a novel architecture in which privacy-aware FL minimizes processing and storage limitations by employing blockchain technology for model aggregation and verification. We present SecureChainFL, a hybrid dual-layer blockchain protocol developed to enhance decentralized learning's efficiency and protection. A public blockchain uses cryptographic proofs to guarantee auditability, while a private blockchain is used for model validation and aggregation. Byzantine fault-tolerant (BFT) consensus and zero-knowledge proofs (ZKP) help to secure the training process against any hostile effects, while homomorphic encryption (HE) and differential privacy (DP) help to secure model updates from privacy breaches. Our experimental results show that SecureChainFL meets the requirements of privacy-sensitive applications such as healthcare, banking, and autonomous systems because it effectively reduces privacy threats and resists model poisoning, increasing scalability. This research makes advances in privacy-preserving AI by implementing secure, effective, and attack-proof deep learning in decentralized environments.

S. Usharani, P. Manju Bala, A. Balachandar, G. Glorindal

Real-World Applications and Use Cases: Blockchain-Federated DeepFex in Media, Healthcare, and Finance

Abstract

Real-world applications are emerging that integrate blockchain, federated learning, and deepfakes to transform content authentication for media, healthcare, and other sectors. All of these technologies are combined to solve the challenges of deepfake detection while preserving privacy and security requirements. This chapter describes examples of real-world implementations of blockchain-federated deepfakes across three broad industries. The research methodology assesses three industry-specific applications by analyzing social media verification systems, hospitals’ medical image security, financial institutions’ authentication frameworks, as well as system architectures, privacy protection solutions, and performance measurements, forming a cost efficiency assessment in the study. In each case, measurable results, solutions, and integration challenges were examined in the research framework. The case study findings indicate successful integrated solutions across all three sectors. This system outperformed more traditional application model practices for media applications when it came to guaranteeing clean verification of content authenticity and reducing the number of false alerts for deepfake detection. Healthcare system implementations have strengthened medical imaging security and telemedicine protection while maintaining HIPAA regulatory compliance. Fintech results in better fraud detection applications and improved customer verification in the financial sector. In addition, common implementation patterns across industries were identified, and reusable solutions that can be leveraged in future deployments. This study demonstrates that deepfake solutions supported by blockchain federation provide viable ways to deploy these technologies in real-world scenarios. This framework ensures that organizations have functional support for expanding their project deployments and immutable features. The lessons learned from the performance numbers are a solid foundation for future use. These discoveries will help in the advancement of secure and privacy-preserving methods of deepfake detection in various fields.

Mamta

Federated Learning for Enhanced Security in 6G-Enabled Internet of Medical Things Systems

Abstract

The Internet of Medical Things (IoMT) represents a convergence of sensors, healthcare devices, and the Internet of Things (IoT), aiming to enhance healthcare services intelligently. Despite its potential, security and privacy concerns have impeded widespread integration, leading to a scarcity of high-quality IoMT datasets. Federated learning (FL), an emerging distributed learning method, offers enhanced security and privacy, making it beneficial for IoMT networks and smart healthcare systems (SHS). In the context of safeguarding critical networks against evolving cyber threats, intrusion detection systems (IDS) and ransomware have become indispensable. However, existing security models pose challenges and are often computationally expensive, particularly for resource-limited medical IoT devices. In this paper, we propose a privacy-preserving FL-based IDS model, designed to identify cyberattacks within IoMT networks. This model enhances the data privacy and security in IoMT to identify the cyberattacks in wearable devices, implantable devices, medical equipment, and logistic systems. The study demonstrates that integrating FL with 6G capabilities results in a robust and expandable security framework for medical infrastructures, protecting patient data while preserving functionality. All things considered, this strategy provides an intelligent, safe, and well-prepared solution for the healthcare systems of the future.

M. Martina Jose Mary, R. Shyamala Devi, M. Devaraj, Matthew Olusegun Adigun

Blockchain-Powered Intellectual Property: A Decentralized Approach to IP Management

Abstract

The Proof-of-Contribution framework will serve as the foundation for the first blockchain-enabled web application specifically designed to provide enhanced protection for intellectual property (IP). Most existing IP systems, including patents and copyrights, are rife with inefficiency, nontransparent, and cannot withstand false claims, which damage the rights of innovators. This application will provide record-keeping for contributions made and rights verification in a transparent and immutable ledger through the application of blockchain technology. All forms of intellectual contributions and patents can be uploaded by users, whereupon the system will conduct cross-verification of submissions against existing documents to determine duplication or unauthorized claims against another patent. After verification, entries will be on the blockchain, securing their persistence (including date-time stamp, contributor details, and ownership metadata). Through this solution, trust will be enhanced, disputes minimized, and attainment of a fair patent filing and validation process that will eventually improve the attribution of innovations will be achieved.

S. Madumidha, G. Abhishek, M. Akilesh Krishnan, K. J. Hemaharshini, P. Sivaranjani, K. Suresh Kumar, Sunday Adeola Ajagbe

Secure and Reliable Healthcare Credential Verification Using Blockchain

Abstract

An online platform that will revolutionize the management and verification of healthcare professionals’ credentials has been developed. The system is developed on the principles of security, efficiency, and user-friendly access. The platform is a product built in Java on a blockchain-based backend system. This ensures maximum protection of data integrity and privacy through advanced encryption algorithms. A well-functioning credential management system is imperative for healthcare organizations as they hold sensitive information and manage risks, including data breaches, identity theft, and credential fraud. The system creates a safe environment to handle personal information pertaining to certificates and qualifications while ensuring strict privacy. The decentralized peer endorsement system is one of the key distinguishing features of the system. Unlike centralized systems, wherein credentials are vetted and certified through peer endorsements throughout the spectrum of trust and accountability, this system discourages fraudulent entries. Furthermore, a two-step verification system further strengthens the security of certifications authenticated from trusted authority providers such as medical boards or regulatory bodies, ensuring that validation of recorded information is done comprehensively and enhances the integrity of the credential verification system.

S. Madumidha, P. Sivaranjani, M. Nirosh Gowda, P. S. Selva Kirubha, K. Thamarai, K. Suresh Kumar, Matthew Olusegun Adigun

Leveraging Blockchain Technology to Combat Deepfakes: A Novel Approach

Abstract

In today’s highly connected world, there are even greater risks facing online media content for security. We have approached this challenge by developing a new security system that brings together three strong technologies: Blockchain for open ledger, InterPlanetary File System (IPFS) for distributed storage, and AI-assisted deep learning for smart verification. We developed it using the VGG16 neural network model on a web platform so it would be efficient and easy to use. We start by completely stripping identifying metadata and deriving important features from the media files, and safely encrypting them by using AES technology. It is akin to a fingerprint system for the digital world. We store the media files on a network of computers using IPFS, while blockchain maintains a record of ownership and changes that cannot be altered for any reason. To verify whether the media is authentic, we visually compare features with cosine similarity as facial recognition does with faces. Extra security measures were employed through BLAKE3 hashing as a one-fingerprint digital fingerprint at every checkpoint. We wanted to integrate a solution to protect digital media from tampering and fraudulent uses, yet as practical as possible in everyday life, generally, an upgrade or improved security system that offers the user ease of use.

Ramesh Yegireddi, Gudla Jayanth Sairam, Vandrangi Sandeep, Mailarabhatla Sirisha, B. L. Sivamani, Moyyi Premsai

Metadata
Accessibility

Title: Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions
Editors: Abhishek Kumar
Dr. Priya Batta
T. Ananth Kumar
S. Oswalt Manoj
Publisher: Springer Nature Singapore
Electronic ISBN: 978-981-9513-94-9
Print ISBN: 978-981-9513-93-2
DOI: https://doi.org/10.1007/978-981-95-1394-9

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Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions

About this book

Table of Contents

Frontmatter

Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions

DeepFex: Detecting DeepFakes in the Digital Age

A Cross-Architecture Evaluation: CNN, LSTM, GANs, and Transformer Models for DeepFake Detection

DeepFex: Explainable and Secure Deepfake Detection System

Federated Learning with Blockchain-Enhanced Model Explainability

Advanced Privacy Measures for Data Sharing in Federated Learning Networks

Enhanced Privacy Preserving Deep Learning Using Blockchain and Federated Learning

Real-World Applications and Use Cases: Blockchain-Federated DeepFex in Media, Healthcare, and Finance

Federated Learning for Enhanced Security in 6G-Enabled Internet of Medical Things Systems

Blockchain-Powered Intellectual Property: A Decentralized Approach to IP Management

Secure and Reliable Healthcare Credential Verification Using Blockchain

Leveraging Blockchain Technology to Combat Deepfakes: A Novel Approach

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