Adding noise to the images can be considered as a form of data augmentation. It can help the model generalize better by introducing more variability into the training data, making it more robust to variations in the input images. This also reduces the risk of overfitting the model to your training data.
The unexpected result you encountered, where the XceptionNet model performs better when blurry noise is added to the image, can be attributed to a phenomenon known as adversarial examples. Adversarial examples are carefully crafted inputs that are designed to deceive machine learning models, causing them to make incorrect predictions.
In your case, the blurry noise you added to the image might have inadvertently perturbed the input data in a manner that made it more distinguishable for the XceptionNet model. This perturbation could have accentuated certain features or patterns that the model is particularly sensitive to, thus aiding its classification accuracy.
There are a few potential explanations for this phenomenon:
It's important to note that adversarial examples and their effects on different models are still areas of active research. The behavior of deep learning models in the presence of such examples can be complex and sometimes unpredictable. Further investigation and experimentation would be required to gain a deeper understanding of the specific dynamics at play in your scenario.
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