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This paper provides a concise overview of digital forensics, detailing its core processes: identification to reporting—and emphasizing methodical approaches for evidence integrity. It examines foundational models and challenges in scientific validation, including a visual taxonomy of research areas.
The study showcases image forensics as a means to highlight specialized techniques, particularly demonstrating how to detect alterations like re-sampling.
A custom deepfake detection model, built with EfficientNet-B4 in PyTorch, is presented. Its methodology involves data preprocessing, transfer learning, and evaluation using standard classification metrics, demonstrating effective performance.
The paper acknowledges limitations, such as a lack of temporal modelling and smaller dataset scale, and suggests future research directions, including integrating temporal dynamics and expanding data diversity. The objective is to combat misinformation by strengthening the model's ability to classify genuine versus manipulated images.
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Cite Article:
"A Research Paper of Digital Forensics: Foundations, Challenges, and Methodologies", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a854-a865, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506099.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator