What is the impact of changing image sources on an image recognition?
In general the accuracy will change (even dramatically as a google research team found out) if only with slight changes in the input image quality.
References:

Understanding How Image Quality Affects Deep Neural Networks


Image quality is an important practical challenge that is often
overlooked in the design of machine vision systems. Commonly, machine
vision systems are trained and tested on high quality image datasets,
yet in practical applications the input images can not be assumed to
be of high quality. Recently, deep neural networks have obtained
state-of-the-art performance on many machine vision tasks. In this
paper we provide an evaluation of 4 state-of-the-art deep neural
network models for image classification under quality distortions. We
consider five types of quality distortions: blur, noise, contrast,
JPEG, and JPEG2000 compression. We show that the existing networks are
susceptible to these quality distortions, particularly to blur and
noise. These results enable future work in developing deep neural
networks that are more invariant to quality distortions.


Does Deep Learning Have Deep Flaws?


A recent study of neural networks found that for every correctly
classified image, one can generate an &quot;adversarial&quot;, visually
indistinguishable image that will be misclassified. This suggests
potential deep flaws in all neural networks, including possibly a
human brain.