What is/are the default filters used by Keras Convolution2d()?
By default, the filters $W$ are initialised randomly using the glorot_uniform method, which draws values from a uniform distribution with positive and negative bounds described as so: 
$$W \sim \mathcal{U}\left(\frac{6}{n_{in} + n_{out}}, \frac{-6}{n_{in} + n_{out}}\right),$$

where $n_{in}$ is the number of units that feed into this unit, and $n_{out}$ is the number of units this result is fed to.

When you are using the network to make a prediction, these filters are applied at each layer of the network. That is, a discrete convolution is performed for each filter on each input image, and the results of these convolutions are fed to the next layer of convolutions (or fully connected layer, or whatever else you might have).

During training, the values in the filters are optimised with backpropogation with respect to a loss function. For classification tasks such as recognising digits, usually the cross entropy loss is used. 
Here's a visualisation of some filters learned in the first layer (top) and the filters learned in the second layer (bottom) of a convolutional network:



As you can see, the first layer filters basically all act as simple edge detectors, while the second layer filters are more complex. As you go deeper into a network, the filters are able to detect more complex shapes. It gets a little tricky to visualise though, as these filters act on images that have been convolved many times already, and probably don't look much like the original natural image.