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500,000 years ago our eyes and vision were probably significantly worse than they are today. As our eyes evolved to capture the world better our brains also evo

by bno1 9y ago

500,000 years ago our eyes and vision were probably significantly worse than they are today. As our eyes evolved to capture the world better our brains also evolved to correct the errors from our eyes. On the other hand, we feed into our neural networks high quality images. It's true that they are low resolution but they don't contain noticeable noise or artifacts. The attack described here is a smart application of salt and pepper noise. It's ineffective on humans because our vision evolved to filter it out, but a network which has seen only noiseless images is helpless. I'm curious whether training the network by adding noise and other mutations to the set would make the network more resilient to this attacks. In other words, it's the training set or the network architecture that's vulnerable here?

avaxzat 9y ago
>I'm curious whether training the network by adding noise and other mutations to the set would make the network more resilient to this attacks. In other words, it's the training set or the network architecture that's vulnerable here? This is called adversarial training and is currently the most popular technique for protecting neural networks against this type of attack. That being said, it doesn't work as well as one would hope: the adversarially trained models are usually still vulnerable to other attacks.