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In addition, we set a constraint on the noise to ensure it is imperceptible to the human eye. This weakness motivated the design of our model – our introduced noise fools deep neural networks into believing that there is nothing to learn from the protected images. If the model believes that an example does not improve its performance – so, it’s an easy example that it has already learned – it will ignore it. Our technique takes advantage of a key weakness in deep machine learning models – they are lazy learners.
The dark side of electronic medical records Huge data sets are readily available, like the 80 Million Tiny Images collection and ImageNet, but they pose significant privacy and bias problems. The abundant ‘free’ data on the Internet has provided an easy solution to this. This deep machine learning is now widely in use – from driving search engines to guiding medical surgery.Ī key challenge for training deep neural networks is that the programs usually require a huge volume of data to learn from. Modern AI systems have been inspired by the workings of the human brain. Researchers use the term “deep neural network” to differentiate sophisticated modern artificial neural networks.
With each example, the program adjusts its parameters slightly to improve the results. Similar to humans, these artificial neural networks learn to perform a task by repeatedly going through examples – like images of cats, if the AI is learning to identify cats. Modern AI systems are based on artificial neural networks that are computer programs that have been inspired by how the human brain works. With this technique, you could potentially simply tag your data with unlearnable noise that prevents it being exploited. The change is very small and imperceptible to human eyes, but it introduces enough ‘noise’ into an image to make it useless for training AI. We have devised a machine learning-based technique that identifies and changes just enough pixels in an image to confuse AI, and turn it to an ‘unlearnable’ image.