An adversarial example tries to trick a neural network by applying a small worst-case perturbation to a real example.
These were also introduced by Ian Goodfellow.
The first two papers introducing adversarial examples are:
- Explaining and Harnessing Adversarial Examples by Ian Goodfellow et al. in 2014
- Intriguing properties of neural networks by Szegedy et al. in 2014
Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
This is used by Szegedy et al in their paper.
Fast Gradient Sign Method
The fast gradient sign method (FGSM) uses the sign of the gradient times a unit vector as the perturbation.
This was proposed by Ian Goodfellow in his paper.
Projected Gradient Descent
Basic idea: Do gradient descent. If you go too far from your example, project it back into your perturbation range.
This was proposed by Madry et al. in their 2017 paper Towards Deep Learning Models Resistant to Adversarial Attacks.
Most defenses focus on generating adversarial examples during training time and training on those adversarial examples.
Below are some alternatives to this approach.
Interval Bound Propagation
Interval Bound Propagation (IBP)
Semantic Analysis, gender identification, grammer error detection