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Forensic method for recognizing the authenticity of artworks using multispectral analysis
Lánský, David ; Mezina, Anzhelika (referee) ; Burget, Radim (advisor)
Detecting forgeries is crucial for protecting the art market and preserving the authenticity of artworks. This thesis focuses on forgery detection using convolutional neural networks (CNNs). The main goal was to develop advanced methods capable of identifying anomalies, and thus potential forgeries, in images with their X-ray photographs. During this research, U-net architectures and binary semantic segmentation techniques were applied, enabling successful anomaly detection. The main contribution of this work is 112 models of four different U-net and U-net++ architectures, which effectively highlight anomalies through the method of binary semantic segmentation. The models were trained on a set of images with their synthetically created X-ray images and artificially generated anomalies. In this way, the models can detect lead spots, nails, layers of hidden paintings, and other defects, while also being able to ignore insignificant elements, such as picture frames and overexposed X-ray images. The testing of the models occurred in two phases. In the first phase, they were evaluated using the IoU metric on a set of 400 synthetically generated data, where in the best cases, they achieved up to 83.5 % IoU. In the second phase, they were evaluated subjectively on images with real X-rays and natural anomalies. This approach combines traditional X-ray techniques with modern computer vision, revealing deviations that might be overlooked during standard visual inspection. By bridging these technologies, this work opens new possibilities for the protection of art collections and provides a solid foundation for further research in the field of art forgery detection using artificial intelligence.
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