Keyword [Deep Image Prior]
Ilyas A, Jalal A, Asteri E, et al. The robust manifold defense: Adversarial training using generative models[J]. arXiv preprint arXiv:1712.09196, 2017.
1. Overview
1.1. Motivation
- the natural image manifold is low-dimensional but the noisy is very high dimensional
In this paper, it proposed a pre-processing step that projects on the range of a generative model using gradient descent (Invert and Classify, INC)
- robust against first-order, substitute model and combined adversarial attacks
- show that adversarial training on the generative manifold can make the classifier robust to these attacks
- INC + deep image prior
1.2. Contribution
- INC. robust against a wide variety of attacks. first-order, substitute models and enhanced attacks combining the two
- formulating min-max optimization problem
- DIP-INC. without pretrained
2. Methods
2.1. INC
2.2. DIP-INC
- The number of steps was empirically tuned in our experiments and depends on the power of the adversary
3. Experiments
3.1. INC-MNIST
3.2. INC-CelebA
3.3. DIP-INC ImageNet
