This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a formal analysis of adversarial training in DQN agents and its performance with respect to the proportion of adversarial perturbations to nominal…
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48 results for “Adversarial Exploration”
AGAC uses an adversary to enhance exploration in complex tasks.
problem Sample inefficiency in complex environments, especially in tasks requiring efficient exploration.
method Integrates an adversary into the actor-critic framework to encourage innovative exploration strategies.
result AGAC leads to more exhaustive exploration and outperforms state-of-the-art methods.
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
SmoothFool efficiently computes smooth adversarial perturbations for deep networks.
problem Vulnerability of deep neural networks to adversarial attacks with specific statistical properties.
method SmoothFool: a general and computationally efficient framework for computing smooth adversarial perturbations.
result Smoothness significantly enhances robustness against adversarial attacks and improves transferability.
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.
Study explores bias-variance in adversarial machine learning.
problem Understanding adversarial machine learning's impact on bias and variance.
method Investigates bias-variance trade-offs in deep neural networks using MSE and cross-entropy.
result Derives bias-variance trade-offs for classification and regression.
This paper benchmarks time-series adversarial defenses and attacks.
problem Adversarial attacks on time-series data.
method Detailed benchmarking of adversarial defense methods in the threat model.
result Adversarial defenses offer robustness against both strong white-box and black-box attacks.
This paper explores the connection between adversarial and knowledge transferability.
problem Understanding the factors affecting knowledge transferability.
method Theoretical analysis and practical metrics for adversarial transferability.
result Adversarial transferability and knowledge transferability are closely related.
Adversarial training shows promise as an approach for training models that are robust towards adversarial perturbation. In this paper, we explore some of the practical challenges of adversarial training. We present a sensitivity analysis that illustrates that the effectiveness of adversarial training hinges on the sett…
This paper explores memorization in adversarial training and proposes a mitigation algorithm.
problem Understanding and mitigating robust overfitting in adversarial training.
method Demonstrated the capacity of deep networks to memorize adversarial examples, analyzed convergence and generalization issues, and proposed a new mitigation algorithm.
result Identified robust overfitting as a significant drawback of adversarial training and proposed a mitigation algorithm.
Regularized policies are robust to adversarial rewards.
problem Understanding the effects of regularization on policy exploration and robustness.
method Using Fenchel duality to derive the dual problem of the regularized RL objective, showing the optimal policy is robust to adversarial rewards.
result Regularized policies are optimal for a reinforcement learning problem under adversarial reward conditions.
Paper tackles continual reinforcement learning challenges with diversity exploration and adversarial self-correction.
problem Challenges in learning new tasks sequentially due to catastrophic forgetting.
method Develops an end-to-end framework (CDAN) combining unsupervised diversity exploration and adversarial self-correction.
result Final result outperforms baseline by 18.35% in NSD and 0.61 in average reward.
Improved exploration in adversarial MDPs via dilated bonuses.
problem Global exploration in adversarial MDPs with local-search policies.
method Dilated bonuses to facilitate global exploration.
result Improved regret bounds in various MDP settings.
Dropout improves GAN outputs by preserving input constraints.
problem Generating multiple outputs from one input.
method Using Dropout in GANs to maintain input constraints.
result Enhanced generation of diverse outputs from a single input.
Most previous works usually explained adversarial examples from several specific perspectives, lacking relatively integral comprehension about this problem. In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors. Pa…
This paper explores adversarial robustness of flow-based generative models.
problem Robustness of flow-based generative models to adversarial attacks.
method Theoretical and empirical analysis of adversarial robustness for simple and complex flow-based models.
result Flow-based generative models are highly sensitive to adversarial attacks, but robustness can be significantly improved using hybrid adversarial training.
AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
Improved ExO method achieves near-optimal bounds in both stochastic and adversarial settings.
problem Finding optimal exploration strategies in online decision-making with limited feedback.
method Exploration by Optimization with hybrid regularizers for locally observable games.
result Achieved nearly optimal bounds of in stochastic and adversarial environments.
A new approach to make classifiers safer by allowing them to abstain from making decisions on adversarial inputs.
problem Making machine learning systems robust against adversarial attacks, especially in safety-critical applications.
method Introducing a novel objective function and a simple baseline for adversarial robustness with abstention, followed by CARL (Combined Abstention Robustness Learning) for joint classifier and abstention region learning.
result Training with CARL results in a more accurate, robust, and efficient classifier than a simple baseline.
Simple regularization methods mimic adversarial training's robustness.
problem Expensive adversarial training for robustness.
method Label smoothing and logit squeezing.
result Achieves strong adversarial robustness without adversarial examples.
This thesis explores robust machine learning against adversarial examples.
problem How to create machine learning systems robust to adversarial examples.
method Theoretical exploration and development of new learning algorithms with robustness guarantees.
result Developed new learning algorithms with provable robustness guarantees.
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised da…
A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are advers…
Study improves resilience against adversarial clean-label attacks in real and noisy settings.
problem Ensuring accurate predictions in the presence of adversarial clean-label samples.
method Sequential learning from a stream of i.i.d. data, allowing abstention for uncertain predictions.
result Theoretical analysis and adaptations for the agnostic setting with a clean-label adversary and noise.
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.
Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polic…
Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial …
Adversarial attacks found to be effective on code models.
problem Adversarial robustness of code models.
method Instantiated adversarial attacks for code, demonstrated vulnerability, and improved robustness.
result Neural models for code are vulnerable to adversarial attacks.
This paper reviews adversarial attacks on ML and DL models.
problem Adversarial attacks on ML and DL models.
method Review of existing adversarial attacks and perturbations.
result Comprehensive understanding of adversarial security attacks on ML and DL.
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.
Survey of adversarial examples in visual machine learning models.
problem Vulnerability of machine learning models to adversarial examples.
method Exploration of various adversarial attack and defense methods.
result Understanding of adversarial attack and defense mechanisms.
The paper identifies potential adversarial samples near decision boundaries of neural networks.
problem Vulnerability of deep neural networks to small perturbations of inputs.
method Developed a method to explore near decision boundaries of trained classifiers to identify potential adversarial samples.
result Potential adversarial samples represent only 61% of the test data but cover more than 82% of adversarial samples produced by iFGSM and 92% of those by DeepFool on CIFAR10.
Improved regret bound for adversarial bandit convex optimisation.
problem Minimizing regret in zeroth-order adversarial bandit convex optimisation.
method Identifying an improved exploratory distribution for convex functions.
result Proved minimax regret bound of .
With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks have been recently found vulnerable to well-designed input samples, called adversarial examples. Adversarial examples are imperceptible to …
The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw bytes extracted from executables. These architectures reach impressive performance with no feature engineering effort involved, but their rob…
Paper detects adversarial attacks in sound classification models.
problem Adversarial attacks threaten data-driven models, especially in sound classification.
method Detects adversarial subspaces in unitary vector domain using chordal distance and generalized Schur decomposition.
result Regularized logistic regression detector outperforms other approaches on benchmark datasets.
New algorithm for linear optimization with adaptive corruption.
problem Stochastic linear optimization under adversarial corruption.
method Algorithm uses Löwner-John's ellipsoid for exploration and divides time into epochs.
result Regret increases linearly with corruption amount.
Adversarial RL recovers agent rewards from financial market data simulations.
problem Recovering agent rewards in volatile financial markets with unknown dynamics.
method Adversarial inverse reinforcement learning in latent space simulations.
result Adversarial RL can robustly recover agent rewards from latent space representations of real market data.
Paper explores fast adversarial training to improve robustness with less computation.
problem Efficiently defending against adversarial examples.
method Integrates simple self-attacks for faster training, focusing on overfitting recovery.
result Shows superior robust accuracy with reduced training time compared to strong adversarial training.
Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.
problem Adversarial robustness of neural networks.
method Replaced ReLU with stochastic LWTA activations, trained with Variational Bayesian and PGD.
result Stochastic LWTA networks achieve state-of-the-art robustness against adversarial attacks.
Study shows how to manipulate VAEs for attacks and assess their robustness.
problem Adversarial attacks on Variational Autoencoders (VAE).
method Examine modifications to VAEs and propose metrics for robustness.
result Metrics to quantify the robustness of VAEs to adversarial attacks.
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmen…
We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adve…
This paper explores adversarial training limits and improves model robustness against norm-bounded perturbations.
problem Understanding and improving adversarial robustness of deep neural networks.
method Systematic study of adversarial training with various factors, including model size, activation functions, and unlabeled data.
result Training robust models that go beyond state-of-the-art results by combining larger models, Swish/SiLU activations, and model weight averaging.
Proposes a new video attack method that multiplies perturbation to improve model robustness.
problem Challenges existing defense methods for video recognition models against additive adversarial attacks.
method Introduces Multiplicative Adversarial Videos (MultAV) to impose perturbation by multiplication.
result Model trained against additive attacks is less robust to MultAV.
Minimalistic attacks reveal deep RL policies' vulnerabilities with little perturbation.
problem Tackling the vulnerability of deep reinforcement learning policies to minimal perturbations.
method Three key settings: black-box policy access, fractional-state adversary, and tactically-chanced attack. Formulated adversarial attacks on six Atari games.
result Deep RL policies can be significantly fooled by minimal perturbations, even in 0.01% of the input state.
Proposes a model to incentivize exploration in web platforms with payments.
problem Learning and social welfare goals of web platforms with myopic users.
method Contextual bandit model with payments to incentivize exploration.
result Achieves sublinear regret while maximizing cumulative social welfare.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.