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Title: Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability

Paper content:
Deep neural networks are known to be vulnerable to adversarial perturbations.
In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems.
From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets.
This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust.
The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently.
Experiments show that our method effectively improves deep model's adversarial robustness.
Deep neural networks achieve state-of-the-art performances on a variety of tasks (LeCun et al., 2015) .
However, neural nets are known to be vulnerable to adversarial examples.
Imperceptibly perturbed inputs can induce erroneous outputs in neural nets (Szegedy et al., 2013) .
In image classification problems of computer vision, previous work has proposed various methods to attack deep models and induce low accuracy (Goodfellow et al., 2015; Madry et al., 2017; Papernot et al., 2016a; Carlini & Wagner, 2017a) .
Whereas multiple defenses against adversarial attacks are developed, they don't ensure safety faced with strong attacking methods.
There are also theories that explain the existence of adversarial examples (Ilyas et al., 2019; Shamir et al., 2019) , but they often fail to fully explain the features and behaviors of this phenomenon.
This makes the study of adversarial attacks important in that it is a threat to real-life machine learning systems (Kurakin et al., 2016) .
In this paper, we propose a dynamical system view on the adversarial robustness of the models, as well as new method that significantly defense adversarial attacks.
Recent works have shown the connection between deep neural networks and dynamical systems (E, 2017; Haber & Ruthotto, 2017; Lu et al., 2017) .
If we regard the neural net as a discretization of an ordinary differential equation (ODE), then training neural nets becomes finding an optimal control of the corresponding discrete dynamical system.
Traditionally, we often treat training neural networks as an unconstrained non-convex optimization problem
where θ denotes the parameters of the model, J denotes the loss function and R denotes the regularizer term, and we solve the problem with (stochastic) gradient-descent based methods (Bottou, 2010; Ruder, 2016) .
In the training process, we feed the network with a batch of training data, and compute the gradient with forward and backward propagation (E. Rumelhart et al., 1986) .
The propagation process resembles solving optimal control problems that tune the parameters to make the output be close to target states.
This viewpoint motivates us to bridge adversarial robustness with Lyapunov stability of a dynamical system, and to train robust networks with algorithms that find stable optimal control.
We will formulate the discussion in later sections.
2 RELATED WORK 2.1 ADVERSARIAL DEFENSE Many defense methods have been proposed to improve the models' adversarial robustness.
The defenses mainly fall into three types: adversarial training (Szegedy et al., 2013; Zhang et al., 2019) , modifying the networks (Gu & Rigazio, 2015; Lyu et al., 2015; Papernot et al., 2016b; Nayebi & Ganguli, 2017; Ross & Doshi-Velez, 2017) , and adding external models (Lee et al., 2017; Akhtar et al., 2017; Gebhart & Schrater, 2017; Xu et al., 2018; Sun et al., 2019) .
Although various defense methods have been developed, a defended deep model is often successfully attacked by newly developed attacks or specific counter-counter measures (Carlini & Wagner, 2017b) .
Therefore, it can be hoped that defenses against general attacks will be devised to make deep learning models (adversarially) robust to real-life threats.
Motivated by the dynamical system view of neural networks, this work bridges adversarial robustness of deep neural models with Lyapunov stability of dynamical systems, and we also propose a method that uses a stable optimal control algorithm to train neural networks to improve the adversarial robustness of deep neural models.
Though the result didn't surpass STOA defense methods, the stable control view of training neural nets points out another direction towards adversarially robust models.
For future work, on the one hand, mathematical analysis on Lyapunov stability of neural models may be studied to provide theoretical understanding of adversarial robustness.
On the other hand, popular platforms for deep learning, e.g., TensorFlow, PyTorch, didn't provide frameworks for optimal control.
We will obtain better results if specific algorithms for SDP are applied to solve the optimization problem.

assistant: text: An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability