Inverting Gradient Attacks Naturally Makes Data Poisons: An Availability Attack on Neural Networks

• URL: http://arxiv.org/abs/2410.21453v1
• Date: Mon, 28 Oct 2024 18:57:15 GMT
• Title: Inverting Gradient Attacks Naturally Makes Data Poisons: An Availability Attack on Neural Networks
• Authors: Wassim Bouaziz, El-Mahdi El-Mhamdi, Nicolas Usunier,
• Abstract summary: Gradient attacks and data poisoning with machine learning algorithms to alter them have been proven to be equivalent in settings. We show how data poisoning can mimic a gradient attack to perform an attack on neural networks.
• Score: 12.80649024603656
• License:
• Abstract: Gradient attacks and data poisoning tamper with the training of machine learning algorithms to maliciously alter them and have been proven to be equivalent in convex settings. The extent of harm these attacks can produce in non-convex settings is still to be determined. Gradient attacks can affect far less systems than data poisoning but have been argued to be more harmful since they can be arbitrary, whereas data poisoning reduces the attacker's power to only being able to inject data points to training sets, via e.g. legitimate participation in a collaborative dataset. This raises the question of whether the harm made by gradient attacks can be matched by data poisoning in non-convex settings. In this work, we provide a positive answer in a worst-case scenario and show how data poisoning can mimic a gradient attack to perform an availability attack on (non-convex) neural networks. Through gradient inversion, commonly used to reconstruct data points from actual gradients, we show how reconstructing data points out of malicious gradients can be sufficient to perform a range of attacks. This allows us to show, for the first time, an availability attack on neural networks through data poisoning, that degrades the model's performances to random-level through a minority (as low as 1%) of poisoned points.

Related papers

• On the Exploitability of Instruction Tuning [103.8077787502381]
  In this work, we investigate how an adversary can exploit instruction tuning to change a model's behavior. We propose textitAutoPoison, an automated data poisoning pipeline. Our results show that AutoPoison allows an adversary to change a model's behavior by poisoning only a small fraction of data.
  arXiv  Detail & Related papers  (2023-06-28T17:54:04Z)
• Autoregressive Perturbations for Data Poisoning [54.205200221427994]
  Data scraping from social media has led to growing concerns regarding unauthorized use of data. Data poisoning attacks have been proposed as a bulwark against scraping. We introduce autoregressive (AR) poisoning, a method that can generate poisoned data without access to the broader dataset.
  arXiv  Detail & Related papers  (2022-06-08T06:24:51Z)
• Indiscriminate Data Poisoning Attacks on Neural Networks [28.09519873656809]
  Data poisoning attacks aim to influence a model by injecting "poisoned" data into the training process. We take a closer look at existing poisoning attacks and connect them with old and new algorithms for solving sequential Stackelberg games. We present efficient implementations that exploit modern auto-differentiation packages and allow simultaneous and coordinated generation of poisoned points.
  arXiv  Detail & Related papers  (2022-04-19T18:57:26Z)
• Indiscriminate Poisoning Attacks Are Shortcuts [77.38947817228656]
  We find that the perturbations of advanced poisoning attacks are almost textbflinear separable when assigned with the target labels of the corresponding samples. We show that such synthetic perturbations are as powerful as the deliberately crafted attacks. Our finding suggests that the emphshortcut learning problem is more serious than previously believed.
  arXiv  Detail & Related papers  (2021-11-01T12:44:26Z)
• Accumulative Poisoning Attacks on Real-time Data [56.96241557830253]
  We show that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects.
  arXiv  Detail & Related papers  (2021-06-18T08:29:53Z)
• Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching [56.280018325419896]
  Data Poisoning attacks modify training data to maliciously control a model trained on such data. We analyze a particularly malicious poisoning attack that is both "from scratch" and "clean label" We show that it is the first poisoning method to cause targeted misclassification in modern deep networks trained from scratch on a full-sized, poisoned ImageNet dataset.
  arXiv  Detail & Related papers  (2020-09-04T16:17:54Z)
• Towards Class-Oriented Poisoning Attacks Against Neural Networks [1.14219428942199]
  Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset. We propose a class-oriented poisoning attack that is capable of forcing the corrupted model to predict in two specific ways. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework.
  arXiv  Detail & Related papers  (2020-07-31T19:27:37Z)
• Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks [74.88735178536159]
  Data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. We observe that data poisoning and backdoor attacks are highly sensitive to variations in the testing setup. We apply rigorous tests to determine the extent to which we should fear them.
  arXiv  Detail & Related papers  (2020-06-22T18:34:08Z)
• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping [36.41173109033075]
  Machine learning algorithms are vulnerable to data poisoning attacks. We study the feasibility of an attack-agnostic defense relying on artifacts common to all poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-26T14:04:16Z)

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.