International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
ADVANCING DEEPFAKE DETECTION: MOBILE APPLICATION WITH DEEP LEARNING Arun KS1*, Juan Shaji Austin2*, Kiran Paulson3*, Kevin Paulson4*, Suzen Saju Kallungal5* 1,2,3,4BTech.Students,Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala 5Assistant Professor,Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala ----------------------------------------------------------------------------***----------------------------------------------------------------------------Abstract - This paper introduces a pioneering approach In this context, this paper presents a groundbreaking apto deepfake detection leveraging the power of mobile platforms through Flutter. We combine state-of-the-art ResNeXt and LSTM models for robust deepfake identification and en- capsulate them within a user-friendly mobile interface. By harnessing the versatility of Flutter’s InAppWebView, our so- lution seamlessly integrates with mobile devices, empowering users with real-time deepfake detection capabilities on their smartphones. Through rigorous evaluation, we demonstrate the efficacy and usability of our approach, marking a significant advancement in the field of mobile-based deepfake detection.
Index Terms-Flutter, Dart, Inappwebview, LSTM, ResNext I. Introduction In recent years, the proliferation of deepfake technology has raised significant concerns regarding the manipulation of digital content and its potential consequences on various aspects of society, including politics, media, and personal privacy. Deepfakes, which refer to synthetic media generated using advanced machine learning techniques, have become increasingly sophisticated, making it challenging to distin- guish between real and manipulated content. As a result, the need for effective deepfake detection mechanisms has become paramount to combat misinformation and protect individuals and organizations from the harmful effects of manipulated media. Traditional approaches to deepfake detection have primarily relied on complex algorithms and computational resources, often restricting their deployment to highperformance com- puting systems. However, with the widespread adoption of mobile devices, there arises an opportunity to democratize deepfake detection by bringing it to the fingertips of everyday users. Mobile platforms offer unparalleled accessibility and convenience, allowing users to verify the authenticity of media content, irrespective of their location or technical expertise.
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proach to deepfake detection tailored specifically for mobile platforms. Our methodology combines cutting-edge deep learning architectures, namely ResNeXt and LSTM, to effectively identify and classify deepfake content. Leveraging the versatility and efficiency of the Flutter framework, we encapsulate our deep learning models within a user-friendly mobile application, providing users with a seamless and intuitive experience for deepfake detection. The integration of Flutter’s InAppWebView further enhances the functionality of our mobile application, enabling users to analyze online media content directly within the app interface. By harnessing the power of mobile devices, our solution empowers users to verify the authenticity of media content, thereby mitigating the spread of misinformation and preserving the integrity of digital communication channels. Throughout this paper, we provide a comprehensive overview of our deepfake detection methodology, detailing the architecture, implementation, and evaluation of our mobile application. We present experimental results demonstrating the effectiveness and efficiency of our approach, as well as discuss the potential implications and future directions of mobile- based deepfake detection technology. Ultimately, our research aims to contribute to the ongoing efforts in combating digital manipulation and fostering trust in the authenticity of media content in the mobile era.
II. BACKGROUND A. Flutter Flutter is an open-source UI software development kit created by Google, primarily used for building natively compiled applications for mobile, web, and desktop from a single codebase. It offers a fast development cycle, expressive UI components, and native performance across multiple platforms. Flutter utilizes the Dart programming language, developed by Google, known for its efficiency and simplicity. Dart is a statically typed language with features like hot reload,
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