Comments (2)
Thank you for the good question.
I didn't use the Dissim term, because my code is a 1-channel attack version. So, the output of the Dissim function is always 0.
The author of the original paper mentioned that the 1-channel attack forces the values of each channel to have the same value.
In the source code, the get_ct() function is used to duplicate the values of a single channel into the values of 3-channels (r, g, b).
The 1-channel attack is more constrained, it might be weaker than the 3-channel attack. But the perturbation of the 1-channel attack tends to seem like a more natural shadow.
Best regards,
Dongbin Na
from deep-learning-paper-review-and-practice.
Thanks! I missed it, this is the 1-channel attack!
from deep-learning-paper-review-and-practice.
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