Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems

• URL: http://arxiv.org/abs/2112.00973v1
• Date: Thu, 2 Dec 2021 04:12:24 GMT
• Title: Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
• Authors: Siyu Wang, Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang and Quan Z. Sheng
• Abstract summary: We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
• Score: 50.758281304737444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks difficult to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several other crafting methods.

Related papers

• Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack [53.032801921915436]
  Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars. Recently, the robustness of skeleton-based HAR methods have been questioned due to their vulnerability to adversarial attacks. We show such threats exist, even when the attacker only has access to the input/output of the model. We propose the very first black-box adversarial attack approach in skeleton-based HAR called BASAR.
  arXiv  Detail & Related papers  (2022-11-21T09:51:28Z)
• Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting [5.084323778393556]
  adversarial training with untargeted attacks is one of the most recognized methods. We find that the naturally imbalanced inter-class semantic similarity makes those hard-class pairs to become the virtual targets of each other. We propose to upweight hard-class pair loss in model optimization, which prompts learning discriminative features from hard classes.
  arXiv  Detail & Related papers  (2022-10-26T22:51:36Z)
• Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems [15.733167372239432]
  We present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning. We have implemented 20 representative adversarial ML attacks on six popular benchmarking datasets. Our results show that the majority of the existing attacks introduce nonnegligible perturbations that are not stealthy to human eyes.
  arXiv  Detail & Related papers  (2022-05-31T16:43:22Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• TREATED:Towards Universal Defense against Textual Adversarial Attacks [28.454310179377302]
  We propose TREATED, a universal adversarial detection method that can defend against attacks of various perturbation levels without making any assumptions. Extensive experiments on three competitive neural networks and two widely used datasets show that our method achieves better detection performance than baselines.
  arXiv  Detail & Related papers  (2021-09-13T03:31:20Z)
• Learning to Detect Adversarial Examples Based on Class Scores [0.8411385346896413]
  We take a closer look at adversarial attack detection based on the class scores of an already trained classification model. We propose to train a support vector machine (SVM) on the class scores to detect adversarial examples. We show that our approach yields an improved detection rate compared to an existing method, whilst being easy to implement.
  arXiv  Detail & Related papers  (2021-07-09T13:29:54Z)
• Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning [95.60856995067083]
  This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms. We propose to perform adversarial defense from two perspectives: 1) adversarial perturbation purification and 2) adversarial perturbation detection. Experimental results show that our detection module effectively shields the ASV by detecting adversarial samples with an accuracy of around 80%.
  arXiv  Detail & Related papers  (2021-06-01T07:10:54Z)
• Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems [47.70973322193384]
  Adversarial attacks pose significant challenges for detecting them at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks.
  arXiv  Detail & Related papers  (2020-06-14T15:41:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.