100% precision in training set with SVM classifier

Hello! I have a Machine Learning doubt:

I am training a SVM classifier to classify binary images (70x70) in two classes. The dataset has 100 images/class.

In traning and test set the precision is 100%.

This makes me verify again the code and I make another experience: adding noise to the images e classifying again. With random images (100% noise) the precision in the training set was 100% e 50% approx.

The 100% training set precision with 100% noise is possible?

In the graph: Precision on test set

x-axis => noise;

y-axis => accuracy;

blue line => training the SVM classifier with noise

green line => training the SVM classifier without noise

[Walter Roberson]

If images that are nothing but noise are classifying at 100% accuracy then you have something wrong with your setup.

[Jose Marques]

Even in the training set?

With 100% noise, the accuracy of training set is 100% e accuracy of test set is 50%.

[Walter Roberson]

You can get 100% accuracy in training of noise if you overfit. If your number of neurons is higher than the number of training datasets then it could potentially "remember" something characteristic of each data set.

[Jose Marques]

It's a SVM but I think the could happen too