Exploring machine learning approaches for efficient image forgery detection

In the digital age, accessible image manipulation raises concerns about authenticity, with forgery techniques threatening personal, journalistic, and security contexts. Detecting alterations is crucial for maintaining trust in visual content. A robust system capable of detecting various types of ima...

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Veröffentlicht in:	Journal of forensic sciences Jg. 70; H. 4; S. 1375 - 1391
Hauptverfasser:	Radhakrishnan, Abilash, Sawant, Tukaram Namdev, Raghuram, Cheepurupalli, Railis, Dani Jermisha, Singh, Harjasdeep
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.07.2025
Schlagworte:	Accuracy Artificial neural networks Authenticity Digital imaging Entropy Entropy (Information theory) Extraction faster region‐based convolutional neural networks Feature extraction Forensic analysis Forgery Fraud Identification methods Image filters image forgery detection Image manipulation Image quality Machine learning machine learning and forensic analysis Manipulation multidimensional spectral hashing Neural networks Pattern recognition Robustness Two‐dimensional maximum Shannon Entropy Median Filter Unsupervised learning Two‐dimensional maximum Shannon Entropy Median Filter machine learning and forensic analysis faster region‐based convolutional neural networks image forgery detection multidimensional spectral hashing
ISSN:	0022-1198, 1556-4029, 1556-4029
Online-Zugang:	Volltext
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