Feedback

[cryptonymous9]

Superpowers:

• The report is quite straightforward to follow and I enjoyed reading it.
• The observation of the tiger-lion weird image is certainly interesting and leaves a lot of questions open.
• The way of adding noise is very similar to how some of the adversarial attacks on vision models are executed and it was paired with good explanations.

Areas of Improvement:

• As you mentioned that you did a manual search and multi-line editing. Python offers very simple commands for such text parsing, so you can look into that.
• While designing a noise to fool the network certainly adds to the evaluation of the robustness of one model but I think it would have been more effective to see if the identified noise was general enough when applied to other pretrained models. Additionally, I wonder how this method performs for models trained with adding noise as its one augmentation.

Price: A

[gnorman7]

Thanks for the feedback! Yeah I don't think the noise would work to fool other models, as I really exploited having information about this model in order to generate it