Machine learning offers great opportunities. It's changing our world and we have to rethink some of our current views. For example, we believe that software without source code is more or less safe from being stolen. In this video, I will show you that this is not the case for some algorithms. In particular, medical imaging device manufacturers try to hide how this software works by using proprietary software and image formats. However, in the age of machine learning, this does not offer sufficient protection. In the following, I will show how we use machine learning to understand the inner workings of a commercial medical image processing method. In our case, we had access to die-conbased image files containing the input data as well as the algorithm's output. Again in die-conform, one main problem was that standard image viewers would only show one scan or channel of the input files. However, we know from the literature that at least two images are required to compute the processed image of the masses that we looked at. So a close look at the input filesize revealed that they are about four times larger than they need to be contained just for a single color channel. So in the next step, we use a software Conrad and it has a feature that war data opener and it allows to load any binary file and interpret it as an image given a certain coding. Here in our case, we knew that the images are 512 by 512 pixels. So using Conrad's drag and drop feature, we quickly figured out the encoding of the image data. This will result in images as shown here. This way, we then figured out that the render is storing four additional images in the header of the file, which also explained the file size. Next, we use Conrad's machine learning features. It uses ImageJ and Vecca to train simple, classical machine learning models. So to demonstrate how easy machine learning is able to replicate the algorithm, we used only a single image slice through the head to train our models. Then we evaluated it three totally different anatomical regions. So for the prediction of mono-energetic images, we could get similarities of the machine learning output and the render algorithm in the range of the numerical compute precision. Moreover, we could show that the linear model is sufficient to achieve this accuracy. Comparison to literature allows us to very precisely tell which method the render actually implement. In a second attack scenario, we investigated a non-linear algorithm. In our case, this is a material separation method. In order to make the task harder, we used a reference that has been processed with additional software, including rescaling and cropping. This implied that we manually had to align the input to the output images. Still, we could train a non-linear machine learning model and achieve astonishing similarity, even on unseen anatomies. The structural similarity was above 0.98 in all cases. So one insight here is that the vendor uses a non-linear model to solve the task. However, if you're only interested in copying, reproducing the vendor's algorithm, we would already be done at this point using this very simple machine learning approach. So obviously, these results have some implications. If you have access to input and output of an image processing method, you can create a copy using machine learning very, very quickly. So as a result, any competitor can very easily reverse engineer these methods with the power of machine learning. Given these observations, I believe that we might reconsider the reasons against open-source software in many applications. If your software can be hacked this easily, why not make it available as open-source directly?