Detecting Deepfake Images and Videos Using Advanced Deep Learning Techniques: Leveraging CNNs and Inception Net Architecture

Detecting Deepfake Images and Videos Using Advanced Deep Learning Techniques: Leveraging CNNs and Inception Net Architecture

Registration ID: IJNRD_224522

Published ID: IJNRD2407029

DOI: Click Here to Get

Kaka Karthik Yadav , P Satish Kumar , Yeddula sreelatha

Deep fake Images, Advanced Deep Learning Techniques, Leveraging CNNs and Inception Net Architecture

Deepfake technology, a rapidly advancing form of synthetic media, poses significant challenges to information integrity and security in today's digital landscape. This paper investigates the detection of deepfake images and videos through the application of advanced deep learning methodologies, specifically focusing on Convolutional Neural Networks (CNNs) and leveraging the InceptionNet architecture. The research aims to develop robust detection systems capable of discerning between genuine and manipulated media, thereby mitigating the proliferation of misinformation and safeguarding societal trust in digital content. The proliferation of deepfake technology has profound implications across various sectors, including politics, finance, and social media. These technologies, enabled by sophisticated machine learning algorithms, allow for the creation of highly realistic yet fabricated media content. Such content can deceive viewers by altering facial expressions, gestures, and contexts in videos, or by manipulating images to depict events that never occurred. The potential for malicious actors to exploit these technologies for misinformation campaigns, financial fraud, or political manipulation. In response to these challenges, researchers have increasingly turned to advanced deep learning techniques to develop effective detection mechanisms for deepfake media. CNNs, a class of deep neural networks particularly adept at processing visual data, have shown promise in automatically learning and extracting intricate patterns and features from images and videos. The InceptionNet architecture, renowned for its ability to capture complex hierarchical features across multiple scales, further enhances the capabilities of CNNs in discerning subtle anomalies indicative of deepfake manipulation. The methodology employed in this study encompasses several key stages: data preprocessing, feature extraction, model training, and real-time processing. Data preprocessing involves cleaning and augmenting raw image and video data from curated datasets such as the DeepFake Detection Challenge (DFDC), ensuring consistency and enhancing model generalization. Feature extraction tasks focus on training CNNs to automatically extract discriminative features from both genuine and deepfake media samples. Transfer learning techniques are leveraged to capitalize on pre-trained CNN models, initially trained on large-scale image datasets like ImageNet, thereby accelerating convergence and improving detection accuracy. Model training involves fine-tuning CNN architectures on labeled datasets, optimizing hyperparameters through iterative experimentation, and rigorously validating performance metrics such as accuracy, precision, recall, and F1 score. Real-time processing capabilities are integrated into the detection pipeline, enabling efficient deployment of detection models across various platforms and applications, including social media platforms and content moderation systems. The results of this research demonstrate a significant achievement, with the proposed deep learning approach achieving a detection accuracy of 93% on the DFDC dataset. This milestone underscores the effectiveness of CNNs and the InceptionNet architecture in successfully distinguishing between genuine and manipulated media, thereby mitigating the risks associated with the dissemination of synthetic media content. In conclusion, the findings of this study highlight the pivotal role of advanced deep learning techniques in addressing the challenges posed by deepfake technology. By leveraging CNNs and the InceptionNet architecture, researchers and practitioners can develop robust detection systems capable of combating the spread of misinformation through manipulated media. Future research directions may include refining detection algorithms to handle evolving deepfake techniques, expanding datasets to encompass diverse scenarios and demographics, and integrating ethical considerations into technological solutions. Ultimately, collaborative efforts across academia, industry, and policymakers are essential to develop comprehensive strategies for mitigating the societal impacts of deepfake technology and preserving trust in digital information.

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"Detecting Deepfake Images and Videos Using Advanced Deep Learning Techniques: Leveraging CNNs and Inception Net Architecture", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.a278-a286, July-2024, Available :https://ijnrd.org/papers/IJNRD2407029.pdf

Volume 9 Issue 7, July-2024

Pages : a278-a286

Paper Reg. ID: IJNRD_224522

Published Paper Id: IJNRD2407029

Downloads: 000121228

Research Area: Computer Engineering 

Country: Cuddapah, Andra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407029.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407029

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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