Adversarial training for tabular data with attack propagation

• URL: http://arxiv.org/abs/2307.15677v1
• Date: Fri, 28 Jul 2023 17:12:46 GMT
• Title: Adversarial training for tabular data with attack propagation
• Authors: Tiago Leon Melo, Jo\~ao Bravo, Marco O. P. Sampaio, Paolo Romano, Hugo Ferreira, Jo\~ao Tiago Ascens\~ao, Pedro Bizarro
• Abstract summary: We propose a new form of adversarial training where attacks are propagated between the two spaces in the training loop. We show that our method can prevent about 30% performance drops under moderate attacks and is essential under very aggressive attacks.
• Score: 1.9826772682131455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them. Adversarially training ML models that are robust against such attacks can prevent business losses and reduce the work load of system maintainers. In such applications data is often tabular and the space available for attackers to manipulate undergoes complex feature engineering transformations, to provide useful signals for model training, to a space attackers cannot access. Thus, we propose a new form of adversarial training where attacks are propagated between the two spaces in the training loop. We then test this method empirically on a real world dataset in the domain of credit card fraud detection. We show that our method can prevent about 30% performance drops under moderate attacks and is essential under very aggressive attacks, with a trade-off loss in performance under no attacks smaller than 7%.

Related papers

• Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats [52.94388672185062]
  We propose an efficient defense mechanism against backdoor threats using a concept known as machine unlearning. This entails strategically creating a small set of poisoned samples to aid the model's rapid unlearning of backdoor vulnerabilities. In the backdoor unlearning process, we present a novel token-based portion unlearning training regime.
  arXiv  Detail & Related papers  (2024-09-29T02:55:38Z)
• A Practical Trigger-Free Backdoor Attack on Neural Networks [33.426207982772226]
  We propose a trigger-free backdoor attack that does not require access to any training data. Specifically, we design a novel fine-tuning approach that incorporates the concept of malicious data into the concept of the attacker-specified class. The effectiveness, practicality, and stealthiness of the proposed attack are evaluated on three real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T08:53:36Z)
• Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks [11.390175856652856]
  Clean-label attacks are a more stealthy form of backdoor attacks that can perform the attack without changing the labels of poisoned data. We study different strategies for selectively poisoning a small set of training samples in the target class to boost the attack success rate. Our threat model poses a serious threat in training machine learning models with third-party datasets.
  arXiv  Detail & Related papers  (2024-07-15T15:38:21Z)
• SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks [53.28390057407576]
  Modern NLP models are often trained on public datasets drawn from diverse sources. Data poisoning attacks can manipulate the model's behavior in ways engineered by the attacker. Several strategies have been proposed to mitigate the risks associated with backdoor attacks.
  arXiv  Detail & Related papers  (2024-05-19T14:50:09Z)
• Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors [26.36344184385407]
  In this paper, we explore the threat of indiscriminate attacks on downstream tasks that apply pre-trained feature extractors. We propose two types of attacks: (1) the input space attacks, where we modify existing attacks to craft poisoned data in the input space; and (2) the feature targeted attacks, where we find poisoned features by treating the learned feature representations as a dataset. Our experiments examine such attacks in popular downstream tasks of fine-tuning on the same dataset and transfer learning that considers domain adaptation.
  arXiv  Detail & Related papers  (2024-02-20T01:12:59Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• RelaxLoss: Defending Membership Inference Attacks without Losing Utility [68.48117818874155]
  We propose a novel training framework based on a relaxed loss with a more achievable learning target. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs.
  arXiv  Detail & Related papers  (2022-07-12T19:34:47Z)
• On the Effectiveness of Adversarial Training against Backdoor Attacks [111.8963365326168]
  A backdoored model always predicts a target class in the presence of a predefined trigger pattern. In general, adversarial training is believed to defend against backdoor attacks. We propose a hybrid strategy which provides satisfactory robustness across different backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-22T02:24:46Z)
• FooBaR: Fault Fooling Backdoor Attack on Neural Network Training [5.639451539396458]
  We explore a novel attack paradigm by injecting faults during the training phase of a neural network in a way that the resulting network can be attacked during deployment without the necessity of further faulting. We call such attacks fooling backdoors as the fault attacks at the training phase inject backdoors into the network that allow an attacker to produce fooling inputs.
  arXiv  Detail & Related papers  (2021-09-23T09:43:19Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks [2.830541450812474]
  We explore the vulnerability of Elastic Weight Consolidation (EWC), a popular continual learning algorithm for avoiding catastrophic forgetting. We show that an intelligent adversary can bypass the EWC's defenses, and instead cause gradual and deliberate forgetting by introducing small amounts of misinformation to the model during training. We demonstrate such an adversary's ability to assume control of the model via injection of "backdoor" attack samples on both permuted and split benchmark variants of the MNIST dataset.
  arXiv  Detail & Related papers  (2020-02-17T18:13:09Z)

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.