PHOTO AUTHENTICITY DETECTION USING MACHINE LEARNING FOR DEEPFAKE AND AI-GENERATED CONTENT VERIFICATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6949Keywords:
Photo Authenticity Detection, Deepfake Identification, AI-Generated Content Verification, Machine Learning for Image Forensics, Synthetic Media Detection, Deep Learning for Visual IntegrityAbstract [English]
The rise of deepfake technologies and AI-generated images has made people very worried about how real visual material on digital platforms really is. Traditional tracking methods are having a hard time keeping up with the sophistication of fake media, which is why advanced, smart proof systems have had to be created. This research shows a complete machine learning system that can tell the difference between real photos and photos that have been changed by AI or are completely fake. The suggested system combines convolutional neural networks (CNNs) for extracting localised features and transformer-based designs for detecting global errors. The models were trained and tested using a carefully chosen collection that included real, deepfake, and AI-generated pictures. Using feature engineering methods, such as frequency domain analysis and noise residual modelling, made recognition even better. In the experiments, the mixed model did better than several state-of-the-art baselines, achieving a classification accuracy of 94.8%, with a precision of 93.6%, a recall of 94.2%, and an F1-score of 93.9%. This study shows how important machine learning is for protecting digital identity and fighting the growing danger of fake media. In the future, researchers will look into explainable AI methods to make models easier to understand and build trust among users.
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Copyright (c) 2025 Rajesh Raikwar, Dr. Amena Ansari, Atul Shrivas, Dr. Archana Santosh Ubale, Adarsh Kumar, Simranjit Kaur
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