Firms have begun to collaborate with computer scientists and researchers to introduce techniques that will empower machine learning models. It will help companies predict potential threats. It will also let organizations know when their machine learning (ML) systems are used without permission. These innovative neural networks will create a unique watermark to keep the safety of machine learning models intact. In his article for Dark Reading, Robert Lemos talks about this new technique and what it means for the future of cybersecurity.
Innovative Area of Research
According to a study conducted by New York University, the shift toward outsourcing in the ML supply chain could help attackers implant malicious source code. Those source codes can be triggered by certain initiation. Many companies do not pay much attention to safeguarding their machine learning models, making them prone to cyberattacks. Artificial intelligence (AI) and machine learning models hold significant value for any company. This is one of the prime reasons why attackers will try to tamper with them to gain hostile access to companies.
Safeguarding Machine Learning Models
To tackle malicious implantations by hackers, researchers have devised the idea of watermark insertion in ML models. It will activate at the slightest doubt of accuracy. In doing so, this will further save the ML algorithms from being tampered with. Some enterprises have begun to explore the future of model watermarking to sell as a service.
How Crucial Are ML Models?
You must be thinking about why it is so important to protect machine learning algorithms from attacks. To get an idea of their importance, you should understand that the installation and maintenance of ML models can cost from thousands of dollars to millions of dollars. ML models hold significant value in a company and are prone to exploitation through any application programming interface. Watermarking is gaining widespread popularity to stop these attacks. However, every company should come up with several innovative measures to tackle the problem.
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