Send us a textTo learn more, visit Art Recognition's website.SHOW NOTES:2:00 Lisa Salama’s background in art and art law3:00 authentication remains main risk of art trading3:30 Salama’s work overseeing legal aspect of Art Recognition and 4:00 analysis process4:15 the deep convolutional neural network mimics human brain5:30 physical inspection of work is not needed6:00 algorythm assessment determines authenticity6:15 heat map shows important features that led to network’s decision in red6:45 Sample report available, e.g., Cezanne’s Boy In Red Vest7:15 Samson & Delilah confirmed as non-authenticate; listed by National Gallery as a Rubens8:40 Collaboration with universities 10:30 data sets12:00 analysis of paintings and drawings13:00 analysis of restored work14:00 legal issues16:30 data set due diligence process18:00 minimum 100 works for a data set19:00 how AI compliments other authentication methods, e.g., art experts21:00 Unsupervised Deep Learning involves no human programming21:40 works created by school of an artist22:30 different styles and periods of an artists’ work23:00 graffiti23:20 range of clients24:00 over 500 case reports completed 24:10 Future for an Art Recognition app24:45 co-creator Dr. Carina Popovici 26:00 how Art Recognition contributes to transparency in art market28:20 Responsible Art MarketPlease share your comments and/or questions at [email protected] by Toulme.To hear more episodes, please visit Warfare of Art and Law podcast's website.To leave questions or comments about this or other episodes of the podcast and/or for information about joining the 2ND Saturday discussion on art, culture and justice, please message me at [email protected]. Thanks so much for listening!© Stephanie Drawdy [2025]

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