Will images modification get me a better machine learning model?
One can never 100% say that a data preprocessing approach will yield positive results. So, if you are trying different things, always test and use the metrics to see what works best.
With that said, what you described is often referred to data augmentation, namely the generation of more data points, typically from existing data points. It is very common practice and so I encourage you to try it on your problem. Common techniques include flipping, shifting, or rotating the original images.
The problem of having few examples per class is called zero- or few-shot learning. Specific techniques such as siamese networks have been devised to tackle these. Here is a good blog to use as reference.