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the authenticity of digital images has become increasingly important in an era where tampering techniques are advancing rapidly. This paper introduces a hybrid approach to image authentication that combines the power of artificial intelligence with traditional feature engineering. By integrating Convolutional Neural Networks (CNNs) with Histogram of Oriented Gradients (HOG), the proposed method leverages deep learning to extract high-level patterns and HOG to capture fine-grained texture and edge details. The fusion of these complementary features enables accurate detection of tampered images, including those with subtle manipulations. Extensive testing on standard datasets demonstrates that this hybrid model outperforms standalone methods in terms of accuracy and robustness. This research provides a practical and scalable solution for enhancing the reliability of image authentication systems across various applications, from digital forensics to media verification.
"AI-Driven Image Authentication: A Hybrid CNN-HOG Approach for Tamper Detection.", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 12, page no.a595-a599, December-2024, Available :http://www.ijrti.org/papers/IJRTI2412064.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