Closed a7b23 closed 5 years ago
Hi Abhishek,
Thank you for your submission!
I am assuming that "53.82% robustness" in the claims can be interpreted as "53.82% accuracy". Please let me know if this is not correct.
One small thing before we add your submission: if I try running your code by running fetch_model.py
followed by using model_robustml.py
, I get an error because the filenames don't match up. It looks like model_robustml.py
looks for the trained model in feature_adv11/checkpoint-1000
, while fetch_model.py
downloads the model to models/adv_trained/...
. Could you please correct this so it's possible to run the robustml model without changing code?
Once this is done, we can add your defense to the list.
Hi Anish,
Yes, "53.82% robustness" corresponds to "53.82% accuracy".
You can get our adversarial robust model feature_adv11/checkpoint-1000
through the google drive link mentioned in the readme of the github repo( adding the link here also - https://drive.google.com/open?id=1um2zoVYYw5YZuuV8_IeoUy-qRWSmCVUb ).
fetch_model.py
fetches the baseline model over which we perform our fine-tuning to obtain our model. fetch_model.py
would be only needed if one wants to reproduce our training methodology.
Ah I see, that makes sense, thanks for explaining it to me.
Added in 70fa746f9525ee40d1004b0ef30e0cc5391468cf.
Name: Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Authors: Abhishek Sinha, Mayank Singh, Nupur Kumari, Balaji Krishnamurthy, Harshitha Machiraju, V N Balasubramaniam
Paper: https://arxiv.org/abs/1905.05186
Code: https://github.com/conference-submission-anon/LAT_adversarial_robustness
Venue: IJCAI 2019
Does the code implement the robust-ml API and include pre-trained models: yes
Dataset: CIFAR-10
Threat model: $l_\infty(\epsilon = 0.03)$
Natural accuracy: 87.8%
Claims: 53.82% robustness against epsilon = 0.03