I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, includi…
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48 results for “adversary control”
New algorithm achieves logarithmic regret for adversarial online control.
problem Online linear-quadratic control in systems with adversarial disturbances.
method Characterization of optimal offline control law, reduced to online learning with approximate advantage functions.
result First algorithm with logarithmic regret for arbitrary adversarial disturbance sequences.
Defense strategy improves controller robustness against adversarial attacks.
problem Adversarial attacks on learning-enabled controllers in CPS.
method Two-stage defense strategy treating controller and environment as black-boxes with unknown dynamics.
result Defense strategy effectively improves controller robustness in realistic control domains.
We consider the problem of controlling a possibly unknown linear dynamical system with adversarial perturbations, adversarially chosen convex loss functions, and partially observed states, known as non-stochastic control. We introduce a controller parametrization based on the denoised observations, and prove that apply…
A method to minimize regret in multi-agent control systems with adversarial disturbances.
problem Optimal control of dynamical systems with adversarial disturbances and multiple agents.
method Reduction from online convex optimization to a distributed algorithm for multi-agent control.
result The resulting distributed algorithm has low regret relative to the optimal precomputed joint policy.
Optimizes deep learning training by treating it as an optimal control problem.
problem Fragility of deep neural networks to adversarial inputs.
method Formulates adversarial training as a min-max optimization problem and interprets it as an optimal control problem.
result Provides the first convergence analysis of adversarial training algorithm.
New method controls linear systems with adversarial disturbances.
problem Controlling linear dynamical systems under adversarial conditions.
method Novel convex relaxation using spectral filters from Hankel matrix eigenvectors.
result Polylogarithmic running time improvement over prior methods.
New adversarial training method improves robustness of power system controllers.
problem Designing robust controllers for complex cyber-physical power systems.
method Adversarial training approach with fixed opponent policy.
result Adversarial trained controllers show useful preventive behaviors in the N-1 problem.
Automated testing framework finds weaknesses in deep control policies.
problem Safety of deep neural network control policies is difficult to validate.
method Adversarial reinforcement learning to test and find weaknesses.
result Framework finds weaknesses not evident during manual testing.
The paper tackles adversarial attacks on recurrent neural networks.
problem Adversarial attacks on recurrent neural networks are easy and lack theoretical guarantees.
method Inspired by dynamical systems theory, the paper dynamically computes adversarial perturbations for each timestep of the input sequence.
result The paper provides theoretical guarantees on the existence of adversarial examples and robustness margins.
We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result…
Paper robustifies reinforcement learning agents against action space perturbations.
problem Vulnerability of reinforcement learning agents to action space perturbations (e.g. actuator attacks).
method Adversarial training to robustify DRL agents against perturbations.
result DRL agents can be robustified against action space perturbations through adversarial training.
We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In contrast to classical control, the a priori determination of an optimal controller here is hindered by the latter's dependence on the yet unkno…
New algorithm achieves optimal regret in non-stochastic control, showing stochasticity is not beneficial.
problem Achieving optimal control in non-stochastic systems with adversarial noise.
method Novel online Newton step algorithm adapted to adversarial disturbances, using policy regret bounds.
result Optimal regret achieved in unknown dynamics, regret in known dynamics.
New method controls linear systems with partial info and disturbances.
problem Controlling linear dynamical systems under partial observation and adversarial disturbances.
method Double Spectral Control (DSC) using two-level spectral approximation strategy.
result Matches best known regret guarantees with exponential runtime improvement.
Framework learns robust control policies from expert demonstrations.
problem Adversarial robustness and closed-loop generalization in feedback control policies.
method Lipschitz-constrained loss minimization for certified robustness and generalization.
result Finite sample bound on policy learning error and robust closed-loop stability.
Machine learning-based IDSs in ICS are vulnerable to adversarial attacks that can bypass them.
problem Adversarial attacks on machine learning-based IDSs in ICS can lead to undetected cyber attacks.
method Used Jacobian-based Saliency Map attack to generate adversarial samples and explored adversarial training to improve model robustness.
result Classification performance of supervised models decreased by 16-20 percentage points with adversarial samples, but improved with adversarial training.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representa…
Study robust control for systems with continuous states using adversarial perturbations.
problem Fragile policies in Markov control models under internal or external perturbations.
method Distributionally robust stochastic control with adaptive adversarial perturbations.
result Optimal robust policies for continuous state systems with uniform learning guarantees.
We describe a novel extension of soft actor-critics for hierarchical Deep Q-Networks (HDQN) architectures using mutual information metric. The proposed extension provides a suitable framework for encouraging explorations in such hierarchical networks. A natural utilization of this framework is an adversarial setting, w…
Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning…
New approach generates optimal disturbances for controller verification.
problem Optimizing disturbances for controller verification with blackbox access.
method Online learning approach that adaptively generates disturbances based on controller inputs.
result New algorithm (MOTR) outperforms existing methods in simulated examples.
Develops a control framework for systemic risk under uncertainty.
problem Systemic risk under model uncertainty.
method Linear-quadratic mean-field control framework with viscosity solutions and verification theorems.
result Explicit feedback controls derived from a coupled Riccati system, preserving analytical tractability.
Deep neural networks are known to be fragile to small adversarial perturbations. This issue becomes more critical when a neural network is interconnected with a physical system in a closed loop. In this paper, we show how to combine recent works on neural network certification tools (which are mainly used in static set…
Efficiently controls unknown linear systems with black-box interactions.
problem Controlling an unknown linear dynamical system from black-box interactions.
method First efficient algorithm with sublinear regret, using robust system identification.
result Resolves open problem on stochastic LQR and black-box LQR control.
Robust RL improves controller robustness to dynamics variations using adversarial populations.
problem Robustness issues in RL when dynamics are perturbed.
method Adversarial population augmentation to the Robust RL formulation.
result Population-based adversarial approach yields more robust and generalizable policies.
Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance …
The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examplesstat.ML
High-dimensional geometry makes adversarial examples easier to construct.
problem Adversarial examples in deep neural networks
method Systematic study of input dimensionality
result Adversarial examples become easier to construct as dimensionality increases.
New method improves neural network robustness to adversarial attacks.
problem Improving adversarial robustness of neural networks.
method Inspired by adaptive control theory, the approach uses persistency of excitation to constrain gradient descent updates.
result Networks trained with the PoE-motivated learning rate schedule are significantly more robust to adversarial attacks.
New method controls bias in training data for fair outcomes.
problem Ensuring equal treatment between different groups in machine learning.
method Contrastive information estimation to control mutual information between representations and protected attributes.
result Our method provides strong theoretical guarantees on the parity of any downstream algorithm.
New control methods for systems with adversarial perturbations.
problem Control systems with adversarial noise.
method Online convex optimization and convex relaxations.
result Low regret policies against adversarial perturbations.
Paper introduces timing-based adversarial attacks on DRL-based navigation systems.
problem Vulnerability of DRL-based navigation systems to adversarial attacks.
method Timing-based adversarial strategies using physical noise patterns.
result Adversarial timing attacks significantly degrade DRL-based navigation performance.
New algorithm controls linear systems with bandit feedback, achieving optimal regret.
problem Controlling linear systems with bandit feedback under adversarial costs.
method Developed a new algorithm for linear control with memory optimization technique.
result Achieved optimal regret growth proportional to square root of time horizon.
Adversaries manipulate wireless power allocation to reduce user rates.
problem Adversaries exploit deep learning for power control to decrease communication rates.
method Adversaries craft perturbations to inputs of a DNN to minimize power allocation.
result Adversarial attacks are highly effective and robust to uncertainties.
WDAIL uses Wasserstein distance for more effective reward shaping in IL.
problem Fixed reward functions in GAIL limit performance on complex tasks.
method Introduces Wasserstein distance and PPO for improved reward shaping and stability.
result Significant performance improvement in complex MuJoCo tasks.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
problem Understanding how learning-based novelty detection methods perform under adversarial perturbations.
method Formulated an oracle attack setup and evaluated vulnerability using black-box adversarial algorithms.
result Adversarial perturbations can significantly increase FDR while maintaining high detection power.
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, acquiring domain-agnostic states is essential for achieving efficient imitation learning. Without these states, imitation learning is hampere…
EigenGAN discovers interpretable dimensions in GAN layers for semantic control.
problem Lack of explicit dimensions to control semantic attributes in GAN layers.
method EigenGAN embeds linear subspaces with orthogonal bases into each generator layer, learning eigen-dimensions corresponding to semantic attributes via adversarial training.
result EigenGAN can produce samples with continuous changes corresponding to specific semantic attributes.
Physics analogies explain machine learning overfitting control.
problem Understanding and controlling overfitting in machine learning.
method Analogies from physics and biology applied to algorithmic stability and GAN models.
result Physics formulas provide insights into reducing overfitting in machine learning.
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system…
New adversarial examples with structured distortion sets improve robustness and perceptibility.
problem Improving adversarial robustness and perceptibility of images.
method Exploring and constraining adversarial search with trace-norms and other norms.
result Structured adversarial perturbations allow larger distortions and control over generation.
New principle controls graph-informed adversarial discrepancies.
problem Graph-informed adversarial learning for interpolative divergences.
method Proves infimal subadditivity for interpolative divergences.
result Graph-informed adversarial learning is justified for interpolative divergences.
AIRL learns robust, generalizable reward functions from demonstrations.
problem Learning robust reward functions from demonstrations for changing environments.
method Adversarial Inverse Reinforcement Learning (AIRL) with hierarchical disentangled rewards.
result Generalizable policies and comparable results to state-of-the-art methods.
Recently introduced generative adversarial network (GAN) has been shown numerous promising results to generate realistic samples. The essential task of GAN is to control the features of samples generated from a random distribution. While the current GAN structures, such as conditional GAN, successfully generate samples…
Achieving Optimal Static and Dynamic Regret Simultaneously in Bandits with Deterministic Lossescs.LG
New algorithm achieves both static and dynamic regret optimally against an oblivious adversary for deterministic losses.
problem Achieving optimal static and dynamic regret simultaneously in adversarial bandits.
method Extends impossibility result to deterministic losses, uses negative static regret and Blackwell approachability.
result First algorithm achieving optimal static and dynamic regret simultaneously against an oblivious adversary.
In many real-world applications of Machine Learning it is of paramount importance not only to provide accurate predictions, but also to ensure certain levels of robustness. Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points. Unfor…
This work improves deep reinforcement learning robustness to adversarial state uncertainty.
problem Robustness of deep reinforcement learning to adversarial state uncertainty.
method Certified adversarial robustness techniques are applied to deep reinforcement learning algorithms to compute guaranteed lower bounds on state-action values.
result The approach increases robustness to noise and adversaries in pedestrian collision avoidance and classic control tasks.
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Toolsstat.ML
Generative Adversarial Networks simulate elevator group control without extensive data.
problem Lack of historical real-world data for system testing.
method Used GANs to generate simulation data for a multi-car elevator system.
result GANs can be used as substitutes for expensive simulation runs, but fine-tuning is needed.