Dear Mr:
I am a student now. I have just studied your codes for about one weeks. I am very interested in your finished paper . I think this paper is of great value to me in the process of studying universal adversarial perturbations.But I have some questions.I will appreciate it if you can explain these problems.
Question 1:
v = v + dr in universal_pert.py(Line 67), the v is the perturbation vectors and dr is the minimal perturbation that fools the classifier. According your codes, the dr have the dimensions with the input image which has three dimensions and v is the perturbation vectors but your setting is v = 0, which means a real value. why can they be added. I wonder the dimensions of v and dr.
Question 2:
dr,iter,, = deepfool(cur_img + v, f, grads, num_classes=num_classes, overshoot=overshoot, max_iter=max_iter_df) in universal_pert.py(Line 63) in which the function deepfool() is in deepfool.py.
w = np.zeros(input_shape) (Line 27) which means the dimensions of w is same with that of input_shape which is 3.
w_k = gradients[k, :, :, :, :] - gradients[0, :, :, :, :] in deepfool.py(Line 40) which means the dimensions of w_k is 4.
w = w_k , why can w_k be assigned to w.
Thank you for taking time out of your busy schedule and look forward to hearing from you soon.
Yours faithfully,
Dear Mr:
I am a student now. I have just studied your codes for about one weeks. I am very interested in your finished paper . I think this paper is of great value to me in the process of studying universal adversarial perturbations.But I have some questions.I will appreciate it if you can explain these problems.
Question 1:
v = v + dr in universal_pert.py(Line 67), the v is the perturbation vectors and dr is the minimal perturbation that fools the classifier. According your codes, the dr have the dimensions with the input image which has three dimensions and v is the perturbation vectors but your setting is v = 0, which means a real value. why can they be added. I wonder the dimensions of v and dr.
Question 2:
dr,iter,, = deepfool(cur_img + v, f, grads, num_classes=num_classes, overshoot=overshoot, max_iter=max_iter_df) in universal_pert.py(Line 63) in which the function deepfool() is in deepfool.py.
w = np.zeros(input_shape) (Line 27) which means the dimensions of w is same with that of input_shape which is 3.
w_k = gradients[k, :, :, :, :] - gradients[0, :, :, :, :] in deepfool.py(Line 40) which means the dimensions of w_k is 4.
w = w_k , why can w_k be assigned to w.
Thank you for taking time out of your busy schedule and look forward to hearing from you soon.
Yours faithfully,