An Unexpected Relationship: Neural Networks and Art
Welcome to my second blog post!
If you didn’t read my last post, I highly recommend reading that one before reading the one below.
As a brief summary, I introduced my project, why I chose it, and what I will be doing. In this post, I’ll be diving into some specifics behind my approach to counterfeit detection using deep learning.
What Have I Been Doing?
Recently, I’ve been immersed in technical research, reading about convolutional neural networks (CNNs) and generative adversarial networks (GANs). These advanced AI models form the backbone of my project: developing a system that can detect fine art counterfeits with a high level of accuracy. The past few weeks have been all about understanding how machine learning processes images and how I can leverage that knowledge for counterfeit detection. Learning the linear algebra, math, and theory behind these models has been tiresome, but, thanks to free online resources from Carnegie Mellon and Harvard, it has been an amazing learning experience where I am learning so much about these complex concepts.
Next week, I’ll begin building and training my first model using open-source datasets of artwork. This is where the real fun begins—testing different architectures and tweaking parameters to see what works best.
How Does it All Work?
Now, let’s discuss what you’ve all been waiting for: the deep science behind AI-driven counterfeit detection.
The fine art market is plagued by forgeries, with estimates suggesting that up to 20% of artworks in circulation are fake. Traditional methods of authentication, such as expert analysis and chemical testing, are time-consuming and prone to human error. That’s where AI comes in.
Deep learning models, particularly CNNs, are excellent at image recognition. By analyzing thousands of paintings, a well-trained CNN can learn to identify unique brushstroke patterns, color distributions, and texture details that distinguish real artwork from fakes. Meanwhile, GANs can generate and compare artistic styles, helping to spot inconsistencies that might go unnoticed by the human eye.
One of the biggest challenges in my research will be acquiring a diverse dataset of verified forgeries and authentic pieces. Since art authentication is a sensitive industry, I’ll need to explore creative ways to augment my dataset—potentially by using synthetic forgeries or transfer learning techniques.
What’s next?
I’ll be experimenting with different CNN architectures and testing how well they perform in distinguishing forgeries from authentic works. My goal is to achieve a reasonable accuracy, while ensuring that my model generalizes well across different artistic styles.
Once I have a working model, I’ll compare its effectiveness against traditional authentication methods. If all goes well, this research could contribute to making art authentication more accessible and reliable.
Thanks for reading, and I hope you read the next post.
Thanks,
Srujan
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