Usually, historians and well-learned art collectors can quickly tell whether the painting is real or fake, but a Russian modern art called avant-garde seems tricky. When the museum in Cologne decided to research avant-garde, the complexity of proving its authenticity unfolded. To find the originality of the painting, the museum has to appoint a separate scientific group. Later through the research, it was revealed that out of 49 artworks, 22 were not authentic. Various new techniques were used to tell the authenticity of the avant-garde paintings. Some popular methods used in the process are electron microscopy and well-known X-ray techniques.
Tracing the originality of a 17th-century artwork may be a very complicated process. Often, manual testing mechanisms may fail to tell the dupes apart from the original ones. So, as technology advances, there is always a scope for newer techniques to come into the picture. There are various kinds of ultra-high range resolution of photography that can even capture the landscape's tiniest of difference. The use of x-ray fluorescence and synchrotron are other ways to tell a painting's originality in a much more affordable and comfortable way.
The best way to explore through all the layers of a painting is to carry out the multispectral imaging process. By employing this technique, one can tell the depth of the pigment and the authenticity of the canvas used. The most amazing discovery to verify the original artwork is done by employing artificial intelligence (AI). The AI protocols used thousands of data from their database to compare, predict, visualize to tell the difference between the original masterpiece and the fake one. The style analysis part done by AI is unbeatable. With these tools, the process of verifying the authenticity of the paintings is made very accurate and easy. There are external applications in the market that can help with the process of authenticating the paintings. Some of them are findexif.com, Foto forensics, pipl.com, etc.
Link to Applications: Fotoforensics | Pipl
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