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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Issue: May 2024
Volume 9 | Issue 5
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| Paper Title: | AI-ForgeryGuard: Image Forgery Detection System |
| Authors Name: | Vandana Dixit , Yash Bhoge , Riya Bongirwar , Vedant Deokar , Sujay Patil |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217769
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| Published Paper Id: | IJNRD2404235 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | The widespread use of digital imaging technology has led to an increase in image manipulation, highlighting the need for reliable forgery detection methods. This paper presents AI-FORGERYGUARD, an innovative approach leveraging Convolutional Neural Networks (CNNs) for accurate and efficient detection of image forgeries. The system automatically learns intricate features from image data, enabling the identification of subtle alterations introduced through various forgery techniques. A comprehensive dataset comprising authentic and manipulated images is used for training and evaluation, ensuring the system's adaptability to real-world scenarios. Performance evaluation against state-of-the-art methods and benchmark datasets is conducted using quantitative metrics such as precision, recall, and F1-score, alongside qualitative analysis. The outcomes of this research contribute significantly to the field of digital forensics by providing a robust and automated solution for detecting image forgeries. AI-FORGERYGUARD has the potential to be integrated into existing image analysis tools, thereby enhancing their capabilities in ensuring the authenticity and integrity of digital visual content. |
| Keywords: | Image forgery detection, Convolutional Neural Networks (CNNs), Deep learning, Digital forensics, Visual content authenticity. |
| Cite Article: | "AI-ForgeryGuard: Image Forgery Detection System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.c52-c56, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404235.pdf |
| Downloads: | 00053 |
| 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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Published Paper ID:IJNRD2404235 Registration ID: 217769 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: c52-c56 Country: Pune, Maharashtra, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404235 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404235 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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